Data Engineering Podcast

Summary Monitoring and auditing IT systems for security events requires the ability to quickly analyze massive volumes of unstructured log data. The majority of products that are available either require too much effort to structure the logs, or aren't fast enough for interactive use cases. Cliff Crosland co-founded Scanner to provide fast querying of high scale log data for security auditing. In this episode he shares the story of how it got started, how it works, and how you can get started with it. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Cliff Crosland about Scanner, a security data lake platform for analyzing security logs and identifying issues quickly and cost-effectively Interview Introduction How did you get involved in the area of data management? Can you describe what Scanner is and the story behind it? What were the shortcomings of other tools that are available in the ecosystem? What is Scanner explicitly not trying to solve for in the security space? (e.g. SIEM) A query engine is useless without data to analyze. What are the data acquisition paths/sources that you are designed to work with?- e.g. cloudtrail logs, app logs, etc. What are some of the other sources of signal for security monitoring that would be valuable to incorporate or integrate with through Scanner? Log data is notoriously messy, with no strictly defined format. How do you handle introspection and querying across loosely structured records that might span multiple sources and inconsistent labelling strategies? Can you describe the architecture of the Scanner platform? What were the motivating constraints that led you to your current implementation? How have the design and goals of the product changed since you first started working on it? Given the security oriented customer base that you are targeting, how do you address trust/network boundaries for compliance with regulatory/organizational policies? What are the personas of the end-users for Scanner? How has that influenced the way that you think about the query formats, APIs, user experience etc. for the prroduct? For teams who are working with Scanner can you describe how it fits into their workflow? What are the most interesting, innovative, or unexpected ways that you have seen Scanner used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Scanner? When is Scanner the wrong choice? What do you have planned for the future of Scanner? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. Links Scanner cURL Rust Splunk S3 AWS Athena Loki Snowflake Podcast Episode Presto [Trino](thttps://trino.io/) AWS CloudTrail GitHub Audit Logs Okta Cribl Vector.dev Tines Torq Jira Linear ECS Fargate SQS Monoid Group Theory Avro Parquet OCSF VPC Flow Logs The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data a

Summary Databases and analytics architectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern customer data platform (CDP). Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro. Your host is Tobias Macey and today I'm interviewing Tasso Argyros about the role of a customer data platform in the context of the modern data stack Interview Introduction How did you get involved in the area of data management? Can you describe what the role of the CDP is in the context of a businesses data ecosystem? What are the core technical challenges associated with building and maintaining a CDP? What are the organizational/business factors that contribute to the complexity of these systems? The early days of CDPs came with the promise of "Customer 360". Can you unpack that concept and how it has changed over the past ~5 years? Recent years have seen the adoption of reverse ETL, cloud data warehouses, and sophisticated product analytics suites. How has that changed the architectural approach to CDPs? How have the architectural shifts changed the ways that organizations interact with their customer data? How have the responsibilities shifted across different roles? What are the governance policy and enforcement challenges that are added with the expansion of access and responsibility? What are the most interesting, innovative, or unexpected ways that you have seen CDPs built/used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDPs? When is a CDP the wrong choice? What do you have planned for the future of ActionIQ? Contact Info LinkedIn @Tasso on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Action IQ Aster Data Teradata Filemaker Hadoop NoSQL Hive Informix Parquet Snowflake Podcast Episode Spark Redshift Unity Catalog Customer Data Platform CDP Market Guide Kaizen The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high qualit

Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. As the sophistication increases, so does the complexity, leading to challenges for user experience. Jignesh Patel has been researching these areas for several years in his work as a professor at Carnegie Mellon University. In this episode he illuminates the landscape of problems that we are faced with and how his research is aimed at helping to solve these problems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Jignesh Patel about the research that he is conducting on technical scalability and user experience improvements around data management Interview Introduction How did you get involved in the area of data management? Can you start by summarizing your current areas of research and the motivations behind them? What are the open questions today in technical scalability of data engines? What are the experimental methods that you are using to gain understanding in the opportunities and practical limits of those systems? As you strive to push the limits of technical capacity in data systems, how does that impact the usability of the resulting systems? When performing research and building prototypes of the projects, what is your process for incorporating user experience into the implementation of the product? What are the main sources of tension between technical scalability and user experience/ease of comprehension? What are some of the positive synergies that you have been able to realize between your teaching, research, and corporate activities? In what ways do they produce conflict, whether personally or technically? What are the most interesting, innovative, or unexpected ways that you have seen your research used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on research of the scalability limits of data systems? What is your heuristic for when a given research project needs to be terminated or productionized? What do you have planned for the future of your academic research? Contact Info Website LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Carnegie Mellon Universe Parallel Databases Genomics Proteomics Moore's Law Dennard Scaling Generative AI Quantum Computing Voltron Data Podcast Episode Von Neumann Architecture Two's Complement Ottertune Podcast Episode dbt Informatica Mozart Data Podcast Episode DataChat Von Neumann Bottleneck The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Do

Summary Working with financial data requires a high degree of rigor due to the numerous regulations and the risks involved in security breaches. In this episode Andrey Korchack, CTO of fintech startup Monite, discusses the complexities of designing and implementing a data platform in that sector. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Andrey Korchak about how to manage data in a fintech environment Interview Introduction How did you get involved in the area of data management? Can you start by summarizing the data challenges that are particular to the fintech ecosystem? What are the primary sources and types of data that fintech organizations are working with? What are the business-level capabilities that are dependent on this data? How do the regulatory and business requirements influence the technology landscape in fintech organizations? What does a typical build vs. buy decision process look like? Fraud prediction in e.g. banks is one of the most well-established applications of machine learning in industry. What are some of the other ways that ML plays a part in fintech? How does that influence the architectural design/capabilities for data platforms in those organizations? Data governance is a notoriously challenging problem. What are some of the strategies that fintech companies are able to apply to this problem given their regulatory burdens? What are the most interesting, innovative, or unexpected approaches to data management that you have seen in the fintech sector? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data in fintech? What do you have planned for the future of your data capabilities at Monite? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Monite ISO 270001 Tesseract GitOps SWIFT Protocol The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Starburst:  This episode is brought to you by

Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Elad Eldor about operating Kafka in production and how to keep your clusters stable and performant Interview Introduction How did you get involved in the area of data management? Can you describe your experiences with Kafka? What are the operational challenges that you have had to overcome while working with Kafka? What motivated to write a book about how to manage Kafka in production? There are many options now for persistent data queues. What are the factors to consider when determining whether Kafka is the right choice? In the case where Kafka is the appropriate tool, there are many ways to run it now. What are the considerations that teams need to work through when determining whether/where/how to operate a cluster? When provisioning a Kafka cluster, what are the requirements that need to be considered when determining the sizing? What are the axes along which size/scale need to be determined? The core promise of Kafka is that it is a durable store for continuous data. What are the mechanisms that are available for preventing data loss? Under what circumstances can data be lost? What are the different failure conditions that cluster operators need to be aware of? What are the monitoring strategies that are most helpful for identifying (proactively or reactively) those errors? In the event of these different cluster errors, what are the strategies for mitigating and recovering from those failures? When a cluster's usage expands beyond the original designed capacity, what are the options/procedures for expanding that capacity? When a cluster is underutilized, how can it be scaled down to reduce cost? What are the most interesting, innovative, or unexpected ways that you have seen Kafka used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kafka? When is Kafka the wrong choice? What are the changes that you would like to see in Kafka to make it easier to operate? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python lang

Summary The "modern data stack" promised a scalable, composable data platform that gave everyone the flexibility to use the best tools for every job. The reality was that it left data teams in the position of spending all of their engineering effort on integrating systems that weren't designed with compatible user experiences. The team at 5X understand the pain involved and the barriers to productivity and set out to solve it by pre-integrating the best tools from each layer of the stack. In this episode founder Tarush Aggarwal explains how the realities of the modern data stack are impacting data teams and the work that they are doing to accelerate time to value. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm welcoming back Tarush Aggarwal to talk about what he and his team at 5x data are building to improve the user experience of the modern data stack. Interview Introduction How did you get involved in the area of data management? Can you describe what 5x is and the story behind it? We last spoke in March of 2022. What are the notable changes in the 5x business and product? What are the notable shifts in the data ecosystem that have influenced your adoption and product direction? What trends are you most focused on tracking as you plan the continued evolution of your offerings? What are the points of friction that teams run into when trying to build their data platform? Can you describe design of the system that you have built? What are the strategies that you rely on to support adaptability and speed of onboarding for new integrations? What are some of the types of edge cases that you have to deal with while integrating and operating the platform implementations that you design for your customers? What is your process for selection of vendors to support? How would you characterize your relationships with the vendors that you rely on? For customers who have pre-existing investment in a portion of the data stack, what is your process for engaging with them to understand how best to support their goals? What are the most interesting, innovative, or unexpected ways that you have seen 5XData used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on 5XData? When is 5X the wrong choice? What do you have planned for the future of 5X? Contact Info LinkedIn @tarush on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign

Summary If your business metrics looked weird tomorrow, would you know about it first? Anomaly detection is focused on identifying those outliers for you, so that you are the first to know when a business critical dashboard isn't right. Unfortunately, it can often be complex or expensive to incorporate anomaly detection into your data platform. Andrew Maguire got tired of solving that problem for each of the different roles he has ended up in, so he created the open source Anomstack project. In this episode he shares what it is, how it works, and how you can start using it today to get notified when the critical metrics in your business aren't quite right. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrew Maguire about his work on the Anomstack project and how you can use it to run your own anomaly detection for your metrics Interview Introduction How did you get involved in the area of data management? Can you describe what Anomstack is and the story behind it? What are your goals for this project? What other tools/products might teams be evaluating while they consider Anomstack? In the context of Anomstack, what constitutes a "metric"? What are some examples of useful metrics that a data team might want to monitor? You put in a lot of work to make Anomstack as easy as possible to get started with. How did this focus on ease of adoption influence the way that you approached the overall design of the project? What are the core capabilities and constraints that you selected to provide the focus and architecture of the project? Can you describe how Anomstack is implemented? How have the design and goals of the project changed since you first started working on it? What are the steps to getting Anomstack running and integrated as part of the operational fabric of a data platform? What are the sharp edges that are still present in the system? What are the interfaces that are available for teams to customize or enhance the capabilities of Anomstack? What are the most interesting, innovative, or unexpected ways that you have seen Anomstack used? What

Summary The first step of data pipelines is to move the data to a place where you can process and prepare it for its eventual purpose. Data transfer systems are a critical component of data enablement, and building them to support large volumes of information is a complex endeavor. Andrei Tserakhau has dedicated his careeer to this problem, and in this episode he shares the lessons that he has learned and the work he is doing on his most recent data transfer system at DoubleCloud. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues for every part of your data workflow, from migration to deployment. Datafold has recently launched a 3-in-1 product experience to support accelerated data migrations. With Datafold, you can seamlessly plan, translate, and validate data across systems, massively accelerating your migration project. Datafold leverages cross-database diffing to compare tables across environments in seconds, column-level lineage for smarter migration planning, and a SQL translator to make moving your SQL scripts easier. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold today! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrei Tserakhau about operationalizing high bandwidth and low-latency change-data capture Interview Introduction How did you get involved in the area of data management? Your most recent project involves operationalizing a generalized data transfer service. What was the original problem that you were trying to solve? What were the shortcomings of other options in the ecosystem that led you to building a new system? What was the design of your initial solution to the problem? What are the sharp edges that you had to deal with to operate and use that initial implementation? What were the limitations of the system as you started to scale it? Can you describe the current architecture of your data transfer platform? What are the capabilities and constraints that you are optimizing for? As you move beyond the initial use case that started you down this path, what are the complexities involved in generalizing to add new functionality or integrate with additional platforms? What are the most interesting, innovative, or unexpected ways that you have seen your data transfer service used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data transfer system? When is DoubleCloud Data Transfer the wrong choice? What do you have planned for the future of DoubleCloud Data Transfer? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our

Summary Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. One of the core complexities that needs to be addressed is the fractal set of integrations that need to be managed across the individual components. In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Developing event-driven pipelines is going to be a lot easier - Meet Functions! Memphis functions enable developers and data engineers to build an organizational toolbox of functions to process, transform, and enrich ingested events “on the fly” in a serverless manner using AWS Lambda syntax, without boilerplate, orchestration, error handling, and infrastructure in almost any language, including Go, Python, JS, .NET, Java, SQL, and more. Go to dataengineeringpodcast.com/memphis today to get started! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'll be sharing an update on my own journey of building a data platform, with a particular focus on the challenges of tool integration and maintaining a single source of truth Interview Introduction How did you get involved in the area of data management? data sharing weight of history existing integrations with dbt switching cost for e.g. SQLMesh de facto standard of Airflow Single source of truth permissions management across application layers Database engine Storage layer in a lakehouse Presentation/access layer (BI) Data flows dbt -> table level lineage orchestration engine -> pipeline flows task based vs. asset based Metadata platform as the logical place for horizontal view Contact Info LinkedIn Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Monologue Episode On Data Platform Design Monologue Episode On Leaky Abstractions Airbyte Podcast Episode Trino Dagster dbt Snowflake BigQuery OpenMetadata OpenLineage Data Platform Shadow IT Episode Preset LightDash Podcast Episode SQLMesh Podcast Episode Airflow Sp

Summary The dbt project has become overwhelmingly popular across analytics and data engineering teams. While it is easy to adopt, there are many potential pitfalls. Dustin Dorsey and Cameron Cyr co-authored a practical guide to building your dbt project. In this episode they share their hard-won wisdom about how to build and scale your dbt projects. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro. Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Dustin Dorsey and Cameron Cyr about how to design your dbt projects Interview Introduction How did you get involved in the area of data management? What was your path to adoption of dbt? What did you use prior to its existence? When/why/how did you start using it? What are some of the common challenges that teams experience when getting started with dbt? How does prior experience in analytics and/or software engineering impact those outcomes? You recently wrote a book to give a crash course in best practices for dbt. What motivated you to invest that time and effort? What new lessons did you learn about dbt in the process of writing the book? The introduction of dbt is largely responsible for catalyzing the growth of "analytics engineering". As practitioners in the space, what do you see as the net result of that trend? What are the lessons that we all need to invest in independent of the tool? For someone starting a new dbt project today, can you talk through the decisions that will be most critical for ensuring future success? As dbt projects scale, what are the elements of technical debt that are most likely to slow down engineers? What are the capabilities in the dbt framework that can be used to mitigate the effects of that debt? What tools or processes outside of dbt can help alleviate the incidental complexity of a large dbt project? What are the most interesting, innovative, or unexpected ways that you have seen dbt used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with dbt? (as engineers and/or as autors) What is on your personal wish-list for the future of dbt (or its competition?)? Contact Info Du

Summary Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Eran Yahav about building an AI powered developer assistant at Tabnine Interview Introduction How did you get involved in machine learning? Can you describe what Tabnine is and the story behind it? What are the individual and organizational motivations for using AI to generate code? What are the real-world limitations of generative AI for creating software? (e.g. size/complexity of the outputs, naming conventions, etc.) What are the elements of skepticism/oversight that developers need to exercise while using a system like Tabnine? What are some of the primary ways that developers interact with Tabnine during their development workflow? Are there any particular styles of software for which an AI is more appropriate/capable? (e.g. webapps vs. data pipelines vs. exploratory analysis, etc.) For natural languages there is a strong bias toward English in the current generation of LLMs. How does that translate into computer languages? (e.g. Python, Java, C++, etc.) Can you describe the structure and implementation of Tabnine? Do you rely primarily on a single core model, or do you have multiple models with subspecialization? How have the design and goals of the product changed since you first started working on it? What are the biggest challenges in building a custom LLM for code? What are the opportunities for specialization of the model architecture given the highly structured nature of the problem domain? For users of Tabnine, how do you assess/monitor th

Summary Databases are the core of most applications, but they are often treated as inscrutable black boxes. When an application is slow, there is a good probability that the database needs some attention. In this episode Lukas Fittl shares some hard-won wisdom about the causes and solution of many performance bottlenecks and the work that he is doing to shine some light on PostgreSQL to make it easier to understand how to keep it running smoothly. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Your host is Tobias Macey and today I'm interviewing Lukas Fittl about optimizing your database performance and tips for tuning Postgres Interview Introduction How did you get involved in the area of data management? What are the different ways that database performance problems impact the business? What are the most common contributors to performance issues? What are the useful signals that indicate performance challenges in the database? For a given symptom, what are the steps that you recommend for determining the proximate cause? What are the potential negative impacts to be aware of when tuning the configuration of your database? How does the database engine influence the methods used to identify and resolve performance challenges? Most of the database engines that are in common use today have been around for decades. How have the lessons learned from running these systems over the years influenced the ways to think about designing new engines or evolving the ones we have today? What are the most interesting, innovative, or unexpected ways that you have seen to address database performance? What are the most interesting, unexpected, or challenging lessons that you have learned while working on databases? What are your goals for the future of database engines? Contact Info LinkedIn @LukasFittl on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the in

Summary Databases are the core of most applications, whether transactional or analytical. In recent years the selection of database products has exploded, making the critical decision of which engine(s) to use even more difficult. In this episode Tanya Bragin shares her experiences as a product manager for two major vendors and the lessons that she has learned about how teams should approach the process of tool selection. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro. Your host is Tobias Macey and today I'm interviewing Tanya Bragin about her views on the database products market Interview Introduction How did you get involved in the area of data management? What are the aspects of the database market that keep you interested as a VP of product? How have your experiences at Elastic informed your current work at Clickhouse? What are the main product categories for databases today? What are the industry trends that have the most impact on the development and growth of different product categories? Which categories do you see growing the fastest? When a team is selecting a database technology for a given task, what are the types of questions that they should be asking? Transactional engines like Postgres, SQL Server, Oracle, etc. were long used as analytical databases as well. What is driving the broad adoption of columnar stores as a separate environment from transactional systems? What are the inefficiencies/complexities that this introduces? How can the database engine used for analytical systems work more closely with the transactional systems? When building analytical systems there are numerous moving parts with intricate dependencies. What is the role of the database in simplifying observability of these applications? What are the most interesting, innovative, or unexpected ways that you have seen Clickhouse used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on database products? What are your prodictions for the future of the database market? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. P

Summary The primary application of data has moved beyond analytics. With the broader audience comes the need to present data in a more approachable format. This has led to the broad adoption of data products being the delivery mechanism for information. In this episode Ranjith Raghunath shares his thoughts on how to build a strategy for the development, delivery, and evolution of data products. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES. This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Your host is Tobias Macey and today I'm interviewing Ranjith Raghunath about tactical elements of a data product strategy Interview Introduction How did you get involved in the area of data management? Can you describe what is encompassed by the idea of a data product strategy? Which roles in an organization need to be involved in the planning and implementation of that strategy? order of operations: strategy -> platform design -> implementation/adoption platform implementation -> product strategy -> interface development managing grain of data in products team organization to support product development/deployment customer communications - what questions to ask? requirements gathering, helping to understand "the art of the possible" What are the most interesting, innovative, or unexpected ways that you have seen organizations approach data product strategies? What are the most interesting, unexpected, or challenging lessons that you have learned while working on defining and implementing data product strategies? When is a data product strategy overkill? What are some additional resources that you recommend for listeners to direct their thinking and learning about data product strategy? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends

Summary Building streaming applications has gotten substantially easier over the past several years. Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. Decodable was built with a mission of eliminating all of the painful aspects of developing and deploying stream processing systems for engineering teams. In this episode Eric Sammer discusses why more companies are including real-time capabilities in their products and the ways that Decodable makes it faster and easier. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES. Your host is Tobias Macey and today I'm interviewing Eric Sammer about starting your stream processing journey with Decodable Interview Introduction How did you get involved in the area of data management? Can you describe what Decodable is and the story behind it? What are the notable changes to the Decodable platform since we last spoke? (October 2021) What are the industry shifts that have influenced the product direction? What are the problems that customers are trying to solve when they come to Decodable? When you launched your focus was on SQL transformations of streaming data. What was the process for adding full Java support in addition to SQL? What are the developer experience challenges that are particular to working with streaming data? How have you worked to address that in the Decodable platform and interfaces? As you evolve the technical and product direction, what is your heuristic for balancing the unification of interfaces and system integration against the ability to swap different components or interfaces as new technologies are introduced? What are the most interesting, innovative, or unexpected ways that you have seen Decodable used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Decodable? When is Decodable the wrong choice? What do you have planned for the future of Decodable? Contact Info esammer on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe t

Summary The insurance industry is notoriously opaque and hard to navigate. Max Cho found that fact frustrating enough that he decided to build a business of making policy selection more navigable. In this episode he shares his journey of data collection and analysis and the challenges of automating an intentionally manual industry. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES. You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Max Cho about the wild world of insurance companies and the challenges of collecting quality data for this opaque industry Interview Introduction How did you get involved in the area of data management? Can you describe what CoverageCat is and the story behind it? What are the different sources of data that you work with? What are the most challenging aspects of collecting that data? Can you describe the formats and characteristics (3 Vs) of that data? What are some of the ways that the operational model of insurance companies have contributed to its opacity as an industry from a data perspective? Can you describe how you have architected your data platform? How have the design and goals changed since you first started working on it? What are you optimizing for in your selection and implementation process? What are the sharp edges/weak points that you worry about in your existing data flows? How do you guard against those flaws in your day-to-day operations? What are the most interesting, innovative, or unexpected ways that you have seen your data sets used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on insurance industry data? When is a purely statistical view of insurance the wrong approach? What do you have planned for the future of CoverageCat's data stack? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with yo

Summary Artificial intelligence applications require substantial high quality data, which is provided through ETL pipelines. Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. In this episode Jay Mishra shares his experiences and insights building ETL pipelines with the help of generative AI. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register at Neo4j.com/NODES. Your host is Tobias Macey and today I'm interviewing Jay Mishra about the applications for generative AI in the ETL process Interview Introduction How did you get involved in the area of data management? What are the different aspects/types of ETL that you are seeing generative AI applied to? What kind of impact are you seeing in terms of time spent/quality of output/etc.? What kinds of projects are most likely to benefit from the application of generative AI? Can you describe what a typical workflow of using AI to build ETL workflows looks like? What are some of the types of errors that you are likely to experience from the AI? Once the pipeline is defined, what does the ongoing maintenance look like? Is the AI required to operate within the pipeline in perpetuity? For individuals/teams/organizations who are experimenting with AI in their data engineering workflows, what are the concerns/questions that they are trying to address? What are the most interesting, innovative, or unexpected ways that you have seen generative AI used in ETL workflows? What are the most interesting, unexpected, or challenging lessons that you have learned while working on ETL and generative AI? When is AI the wrong choice for ETL applications? What are your predictions for future applications of AI in ETL and other data engineering practices? Contact Info LinkedIn @MishraJay on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) wit

Summary The rapid growth of machine learning, especially large language models, have led to a commensurate growth in the need to store and compare vectors. In this episode Louis Brandy discusses the applications for vector search capabilities both in and outside of AI, as well as the challenges of maintaining real-time indexes of vector data. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! If you’re a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex’s magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It’s like having an analytics co-pilot built right into where you’re already doing your work. Then, when you’re ready to share, you can use Hex’s drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial of the Hex Team plan! Your host is Tobias Macey and today I'm interviewing Louis Brandy about building vector indexes in real-time for analytics and AI applications Interview Introduction How did you get involved in the area of data management? Can you describe what vector search is and how it differs from other search technologies? What are the technical challenges related to providing vector search? What are the applications for vector search that merit the added complexity? Vector databases have been gaining a lot of attention recently with the proliferation of LLM applications. Is a dedicated database technology required to support vector indexes/vector search queries? What are the use cases for native vector data types that are separate from AI? With the increasing usage of vectors for data and AI/ML applications, who do you typically see as the owner of that problem space? (e.g. data engineers, ML engineers, data scientists, etc.) For teams who are investing in vector search, what are the architectural considerations that they need to be aware of? How does it impact the data pipeline strategies/topologies used? What are the complexities that need to be addressed when updating vector data in a real-time/streaming fashion? How does that influence the client strategies that are querying that data? What are the most interesting, innovative, or unexpected ways that you have seen vector search used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on vector search applications? When is vector search the wrong choice? What do you se

Summary A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! If you’re a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex’s magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It’s like having an analytics co-pilot built right into where you’re already doing your work. Then, when you’re ready to share, you can use Hex’s drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial of the Hex Team plan! Your host is Tobias Macey and today I'm interviewing Brian Platz about using JSON-LD for building linked-data products Interview Introduction How did you get involved in the area of data management? Can you describe what the term "linked data product" means and some examples of when you might build one? What is the overlap between knowledge graphs and "linked data products"? What is JSON-LD? What are the domains in which it is typically used? How does it assist in developing linked data products? what are the characteristics that distinguish a knowledge graph from What are the layers/stages of applications and data that can/should incorporate JSON-LD as the representation for records and events? What is the level of native support/compatibiliity that you see for JSON-LD in data systems? What are the modeling exercises that are necessary to ensure useful and appropriate linkages of different records within and between products and organizations? Can you describe the workflow for building autonomous linkages across data assets that are modelled as JSON-LD? What are the most interesting, innovative, or unexpected ways that you have seen JSON-LD used for data workflows? What are the most interesting, unexpected, or challenging lessons that you have learned while working on linked data products? When is JSON-LD the wrong choice? What are the future directions that you would like to see for JSON-LD and linked data in the data ecosystem? Contact Info LinkedIn Parting Question From your perspective, what is the biggest

Summary Data systems are inherently complex and often require integration of multiple technologies. Orchestrators are centralized utilities that control the execution and sequencing of interdependent operations. This offers a single location for managing visibility and error handling so that data platform engineers can manage complexity. In this episode Nick Schrock, creator of Dagster, shares his perspective on the state of data orchestration technology and its application to help inform its implementation in your environment. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm welcoming back Nick Schrock to talk about the state of the ecosystem for data orchestration Interview Introduction How did you get involved in the area of data management? Can you start by defining what data orchestration is and how it differs from other types of orchestration systems? (e.g. container orchestration, generalized workflow orchestration, etc.) What are the misconceptions about the applications of/need for/cost to implement data orchestration? How do those challenges of customer education change across roles/personas? Because of the multi-faceted nature of data in an organization, how does that influence the capabilities and interfaces that are needed in an orchestration engine? You have been working on Dagster for five years now. How have the requirements/adoption/application for orchestrators changed in that time? One of the challenges for any orchestration engine is to balance the need for robust and extensible core capabilities with a rich suite of integrations to the broader data ecosystem. What are the factors that you have seen make the most influence in driving adoption of a given engine? What are the most interesting, innovative, or unexpected ways that you have seen data orchestration implemented and/or used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration? When is a data orchestrator the wrong choice? What do you have planned for the future of orchestration with Dagster? Contact Info @schrockn on Twitter LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Dagster GraphQL K8s == Kubernetes Airbyte Podcast Episode High

Summary Cloud data warehouses and the introduction of the ELT paradigm has led to the creation of multiple options for flexible data integration, with a roughly equal distribution of commercial and open source options. The challenge is that most of those options are complex to operate and exist in their own silo. The dlt project was created to eliminate overhead and bring data integration into your full control as a library component of your overall data system. In this episode Adrian Brudaru explains how it works, the benefits that it provides over other data integration solutions, and how you can start building pipelines today. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Your host is Tobias Macey and today I'm interviewing Adrian Brudaru about dlt, an open source python library for data loading Interview Introduction How did you get involved in the area of data management? Can you describe what dlt is and the story behind it? What is the problem you want to solve with dlt? Who is the target audience? The obvious comparison is with systems like Singer/Meltano/Airbyte in the open source space, or Fivetran/Matillion/etc. in the commercial space. What are the complexities or limitations of those tools that leave an opening for dlt? Can you describe how dlt is implemented? What are the benefits of building it in Python? How have the design and goals of the project changed since you first started working on it? How does that language choice influence the performance and scaling characteristics? What problems do users solve with dlt? What are the interfaces available for extending/customizing/integrating with dlt? Can you talk through the process of adding a new source/destination? What is the workflow for someone building a pipeline with dlt? How does the experience scale when supporting multiple connections? Given the limited scope of extract and load, and the composable design of dlt it seems like a purpose built companion to dbt (down to the naming). What are the benefits of using those tools in combination? What are the most interesting, innovative, or unexpected ways that you have seen dlt used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on dlt? When is dlt the wrong choice? What do you have planned for the future of dlt? Contact Info LinkedIn Join our community to discuss further Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email host

Summary Data persistence is one of the most challenging aspects of computer systems. In the era of the cloud most developers rely on hosted services to manage their databases, but what if you are a cloud service? In this episode Vignesh Ravichandran explains how his team at Cloudflare provides PostgreSQL as a service to their developers for low latency and high uptime services at global scale. This is an interesting and insightful look at pragmatic engineering for reliability and scale. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Vignesh Ravichandran about building an internal database as a service platform at Cloudflare Interview Introduction How did you get involved in the area of data management? Can you start by describing the different database workloads that you have at Cloudflare? What are the different methods that you have used for managing database instances? What are the requirements and constraints that you had to account for in designing your current system? Why Postgres? optimizations for Postgres simplification from not supporting multiple engines limitations in postgres that make multi-tenancy challenging scale of operation (data volume, request rate What are the most interesting, innovative, or unexpected ways that you have seen your DBaaS used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on your internal database platform? When is an internal database as a service the wrong choice? What do you have planned for the future of Postgres hosting at Cloudflare? Contact Info LinkedIn Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Cloudflare PostgreSQL Podcast Episode IP Address Data Type in Postgres CockroachDB Podcast Episode Citus Podcast Episode Yugabyte Podcast Episode Stolon pg_rewind PGBouncer HAProxy Presentation Etcd Patroni pg_upgrade Edge Computing The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of buildi

Summary Generative AI has unlocked a massive opportunity for content creation. There is also an unfulfilled need for experts to be able to share their knowledge and build communities. Illumidesk was built to take advantage of this intersection. In this episode Greg Werner explains how they are using generative AI as an assistive tool for creating educational material, as well as building a data driven experience for learners. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Greg Werner about building IllumiDesk, a data-driven and AI powered online learning platform Interview Introduction How did you get involved in the area of data management? Can you describe what Illumidesk is and the story behind it? What are the challenges that educators and content creators face in developing and maintaining digital course materials for their target audiences? How are you leaning on data integrations and AI to reduce the initial time investment required to deliver courseware? What are the opportunities for collecting and collating learner interactions with the course materials to provide feedback to the instructors? What are some of the ways that you are incorporating pedagogical strategies into the measurement and evaluation methods that you use for reports? What are the different categories of insights that you need to provide across the different stakeholders/personas who are interacting with the platform and learning content? Can you describe how you have architected the Illumidesk platform? How have the design and goals shifted since you first began working on it? What are the strategies that you have used to allow for evolution and adaptation of the system in order to keep pace with the ecosystem of generative AI capabilities? What are the failure modes of the content generation that you need to account for? What are the most interesting, innovative, or unexpected ways that you have seen Illumidesk used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Illumidesk? When is Illumidesk the wrong choice? What do you have planned for the future of Illumidesk? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Illumi

Summary Data pipelines are the core of every data product, ML model, and business intelligence dashboard. If you're not careful you will end up spending all of your time on maintenance and fire-fighting. The folks at Rivery distilled the seven principles of modern data pipelines that will help you stay out of trouble and be productive with your data. In this episode Ariel Pohoryles explains what they are and how they work together to increase your chances of success. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Your host is Tobias Macey and today I'm interviewing Ariel Pohoryles about the seven principles of modern data pipelines Interview Introduction How did you get involved in the area of data management? Can you start by defining what you mean by a "modern" data pipeline? At Rivery you published a white paper identifying seven principles of modern data pipelines: Zero infrastructure management ELT-first mindset Speaks SQL and Python Dynamic multi-storage layers Reverse ETL & operational analytics Full transparency Faster time to value What are the applications of data that you focused on while identifying these principles? How do the application of these principles influence the ability of organizations and their data teams to encourage and keep pace with the use of data in the business? What are the technical components of a pipeline infrastructure that are necessary to support a "modern" workflow? How do the technologies involved impact the organizational involvement with how data is applied throughout the business? When using managed services, what are the ways that the pricing model acts to encourage/discourage experimentation/exploration with data? What are the most interesting, innovative, or unexpected ways that you have seen these seven principles implemented/applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working with customers to adapt to these principles? What are the cases where some/all of these principles are undesirable/impractical to implement? What are the opportunities for further advancement/sophistication in the ways that teams work with and gain value from data? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Rivery 7 Principles Of The Modern Data Pipeline ELT Reverse ETL Martech Landscape Data Lakehouse Databricks Snowflake The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Datafold:  This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and st

Summary As businesses increasingly invest in technology and talent focused on data engineering and analytics, they want to know whether they are benefiting. So how do you calculate the return on investment for data? In this episode Barr Moses and Anna Filippova explore that question and provide useful exercises to start answering that in your company. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Barr Moses and Anna Filippova about how and whether to measure the ROI of your data team Interview Introduction How did you get involved in the area of data management? What are the typical motivations for measuring and tracking the ROI for a data team? Who is responsible for collecting that information? How is that information used and by whom? What are some of the downsides/risks of tracking this metric? (law of unintended consequences) What are the inputs to the number that constitutes the "investment"? infrastructure, payroll of employees on team, time spent working with other teams? What are the aspects of data work and its impact on the business that complicate a calculation of the "return" that is generated? How should teams think about measuring data team ROI? What are some concrete ROI metrics data teams can use? What level of detail is useful? What dimensions should be used for segmenting the calculations? How can visibility into this ROI metric be best used to inform the priorities and project scopes of the team? With so many tools in the modern data stack today, what is the role of technology in helping drive or measure this impact? How do your respective solutions, Monte Carlo and dbt, help teams measure and scale data value? With generative AI on the upswing of the hype cycle, what are the impacts that you see it having on data teams? What are the unrealistic expectations that it will produce? How can it speed up time to delivery? What are the most interesting, innovative, or unexpected ways that you have seen data team ROI calculated and/or used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on measuring the ROI of data teams? When is measuring ROI the wrong choice? Contact Info Barr LinkedIn Anna LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Monte Carlo Podcast Episode dbt Podcast Episode JetBlue Snowflake Con Presentation Generative AI Large Language Models The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)Support Data Engineering Podcast

Summary All software systems are in a constant state of evolution. This makes it impossible to select a truly future-proof technology stack for your data platform, making an eventual migration inevitable. In this episode Gleb Mezhanskiy and Rob Goretsky share their experiences leading various data platform migrations, and the hard-won lessons that they learned so that you don't have to. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Modern data teams are using Hex to 10x their data impact. Hex combines a notebook style UI with an interactive report builder. This allows data teams to both dive deep to find insights and then share their work in an easy-to-read format to the whole org. In Hex you can use SQL, Python, R, and no-code visualization together to explore, transform, and model data. Hex also has AI built directly into the workflow to help you generate, edit, explain and document your code. The best data teams in the world such as the ones at Notion, AngelList, and Anthropic use Hex for ad hoc investigations, creating machine learning models, and building operational dashboards for the rest of their company. Hex makes it easy for data analysts and data scientists to collaborate together and produce work that has an impact. Make your data team unstoppable with Hex. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial for your team! Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy and Rob Goretsky about when and how to think about migrating your data stack Interview Introduction How did you get involved in the area of data management? A migration can be anything from a minor task to a major undertaking. Can you start by describing what constitutes a migration for the purposes of this conversation? Is it possible to completely avoid having to invest in a migration? What are the signals that point to the need for a migration? What are some of the sources of cost that need to be accounted for when considering a migration? (both in terms of doing one, and the costs of not doing one) What are some signals that a migration is not the right solution for a perceived problem? Once the decision has been made that a migration is necessary, what are the questions that the team should be asking to determine the technologies to move to and the sequencing of execution? What are the preceding tasks that should be completed before starting the migration to ensure there is no breakage downstream of the changing component(s)? What are some of the ways that a migration effort might fail? What are the major pitfalls that teams need to be aware of as they work through a data platform migration? What are the opportunities for automation during the migration process? What are the most interesting, innovative, or unexpected ways that you have seen teams approach a platform migration? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data platform migrations? What are some ways that the technologies and patterns that we use can be evolved to reduce the cost/impact/need for migraitons? Contact Info Gleb LinkedIn @glebmm on Twitter Rob LinkedIn RobGoretsky on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Datafold Podcast Episode Informatica Airflow Snowflake Podcast Episode Redshift Eventbrite Teradata BigQuery Trino EMR == Elastic Map-Reduce Shadow IT Podcast Episode Mode Analytics Looker Sunk Cost Fallacy data-diff Podcast Episode SQLGlot [Dagster](dhttps://dagster.io/) dbt The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Hex:  Hex is a collaborative workspa

Summary Real-time data processing has steadily been gaining adoption due to advances in the accessibility of the technologies involved. Despite that, it is still a complex set of capabilities. To bring streaming data in reach of application engineers Matteo Pelati helped to create Dozer. In this episode he explains how investing in high performance and operationally simplified streaming with a familiar API can yield significant benefits for software and data teams together. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Modern data teams are using Hex to 10x their data impact. Hex combines a notebook style UI with an interactive report builder. This allows data teams to both dive deep to find insights and then share their work in an easy-to-read format to the whole org. In Hex you can use SQL, Python, R, and no-code visualization together to explore, transform, and model data. Hex also has AI built directly into the workflow to help you generate, edit, explain and document your code. The best data teams in the world such as the ones at Notion, AngelList, and Anthropic use Hex for ad hoc investigations, creating machine learning models, and building operational dashboards for the rest of their company. Hex makes it easy for data analysts and data scientists to collaborate together and produce work that has an impact. Make your data team unstoppable with Hex. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial for your team! Your host is Tobias Macey and today I'm interviewing Matteo Pelati about Dozer, an open source engine that includes data ingestion, transformation, and API generation for real-time sources Interview Introduction How did you get involved in the area of data management? Can you describe what Dozer is and the story behind it? What was your decision process for building Dozer as open source? As you note in the documentation, Dozer has overlap with a number of technologies that are aimed at different use cases. What was missing from each of them and the center of their Venn diagram that prompted you to build Dozer? In addition to working in an interesting technological cross-section, you are also targeting a disparate group of personas. Who are you building Dozer for and what were the motivations for that vision? What are the different use cases that you are focused on supporting? What are the features of Dozer that enable engineers to address those uses, and what makes it preferable to existing alternative approaches? Can you describe how Dozer is implemented? How have the design and goals of the platform changed since you first started working on it? What are the architectural "-ilities" that you are trying to optimize for? What is involved in getting Dozer deployed and integrated into an existing application/data infrastructure? How can teams who are using Dozer extend/integrate with Dozer? What does the development/deployment workflow look like for teams who are building on top of Dozer? What is your governance model for Dozer and balancing the open source project against your business goals? What are the most interesting, innovative, or unexpected ways that you have seen Dozer used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dozer? When is Dozer the wrong choice? What do you have planned for the future of Dozer? Contact Info LinkedIn @pelatimtt on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Dozer Data Robot Netflix Bulldozer CubeJS Podcast Episode JVM == Java Virtual Machine Flink Podcast Episode Airbyte Podcast Episode Fivetran Podcast Episode Delta Lake Podcast Episode LMDB Vector Database LLM == Large Language Model Rockset Podcast Episode Tinybird Podcast Episode Rust Language Material

Summary Data has been one of the most substantial drivers of business and economic value for the past few decades. Bob Muglia has had a front-row seat to many of the major shifts driven by technology over his career. In his recent book "Datapreneurs" he reflects on the people and businesses that he has known and worked with and how they relied on data to deliver valuable services and drive meaningful change. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Bob Muglia about his recent book about the idea of "Datapreneurs" and the role of data in the modern economy Interview Introduction How did you get involved in the area of data management? Can you describe what your concept of a "Datapreneur" is? How is this distinct from the common idea of an entreprenur? What do you see as the key inflection points in data technologies and their impacts on business capabilities over the past ~30 years? In your role as the CEO of Snowflake you had a first-row seat for the rise of the "modern data stack". What do you see as the main positive and negative impacts of that paradigm? What are the key issues that are yet to be solved in that ecosmnjjystem? For technologists who are thinking about launching new ventures, what are the key pieces of advice that you would like to share? What do you see as the short/medium/long-term impact of AI on the technical, business, and societal arenas? What are the most interesting, innovative, or unexpected ways that you have seen business leaders use data to drive their vision? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Datapreneurs book? What are your key predictions for the future impact of data on the technical/economic/business landscapes? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Datapreneurs Book SQL Server Snowflake Z80 Processor Navigational Database System R Redshift Microsoft Fabric Databricks Looker Fivetran Podcast Episode Databricks Unity Catalog RelationalAI 6th Normal Form Pinecone Vector DB Podcast Episode Perplexity AI The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)Support Data Engineering Podcast

Summary For business analytics the way that you model the data in your warehouse has a lasting impact on what types of questions can be answered quickly and easily. The major strategies in use today were created decades ago when the software and hardware for warehouse databases were far more constrained. In this episode Maxime Beauchemin of Airflow and Superset fame shares his vision for the entity-centric data model and how you can incorporate it into your own warehouse design. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Max Beauchemin about the concept of entity-centric data modeling for analytical use cases Interview Introduction How did you get involved in the area of data management? Can you describe what entity-centric modeling (ECM) is and the story behind it? How does it compare to dimensional modeling strategies? What are some of the other competing methods Comparison to activity schema What impact does this have on ML teams? (e.g. feature engineering) What role does the tooling of a team have in the ways that they end up thinking about modeling? (e.g. dbt vs. informatica vs. ETL scripts, etc.) What is the impact on the underlying compute engine on the modeling strategies used? What are some examples of data sources or problem domains for which this approach is well suited? What are some cases where entity centric modeling techniques might be counterproductive? What are the ways that the benefits of ECM manifest in use cases that are down-stream from the warehouse? What are some concrete tactical steps that teams should be thinking about to implement a workable domain model using entity-centric principles? How does this work across business domains within a given organization (especially at "enterprise" scale)? What are the most interesting, innovative, or unexpected ways that you have seen ECM used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on ECM? When is ECM the wrong choice? What are your predictions for the future direction/adoption of ECM or other modeling techniques? Contact Info mistercrunch on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Entity Centric Modeling Blog Post Max's Previous Apperances Defining Data Engineering with Maxime Beauchemin Self Service Data Exploration And Dashboarding With Superset Exploring The Evolving Role Of Data Engineers Alumni Of AirBnB's Early Years Reflect On What They Learned About Building Data Driven Organizations Apache Airflow Apache Superset Preset Ubisoft Ralph Kimball The Rise Of The Data Engineer The Downfall Of The Data Engineer The Rise Of The Data Scientist Dimensional Data Modeling Star Schema Database Normalization Feature Engineering DRY == Don't Repeat Yourself Activity Schema Podcast Episode Corporate Information Factory (affiliate link) The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)Support Data Engineering Podcast

Summary Feature engineering is a crucial aspect of the machine learning workflow. To make that possible, there are a number of technical and procedural capabilities that must be in place first. In this episode Razi Raziuddin shares how data engineering teams can support the machine learning workflow through the development and support of systems that empower data scientists and ML engineers to build and maintain their own features. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Razi Raziuddin about how data engineers can empower data scientists to develop and deploy better ML models through feature engineering Interview Introduction How did you get involved in the area of data management? What is feature engineering is and why/to whom it matters? A topic that commonly comes up in relation to feature engineering is the importance of a feature store. What are the tradeoffs for that to be a separate infrastructure/architecture component? What is the overall lifecycle of a feature, from definition to deployment and maintenance? How is this distinct from other forms of data pipeline development and delivery? Who are the participants in that workflow? What are the sharp edges/roadblocks that typically manifest in that lifecycle? What are the interfaces that are needed for data scientists/ML engineers to be able to self-serve their feature management? What is the role of the data engineer in supporting those interfaces? What are the communication/collaboration channels that are necessary to make the overall process a success? From an implementation/architecture perspective, what are the patterns that you have seen teams build around for feature development/serving? What are the most interesting, innovative, or unexpected ways that you have seen feature platforms used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on feature engineering? What are the resources that you find most helpful in understanding and designing feature platforms? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links FeatureByte DataRobot Feature Store Feast Feature Store Feathr Kaggle Yann LeCun The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)Support Data Engineering Podcast

Summary Data transformation is a key activity for all of the organizational roles that interact with data. Because of its importance and outsized impact on what is possible for downstream data consumers it is critical that everyone is able to collaborate seamlessly. SQLMesh was designed as a unifying tool that is simple to work with but powerful enough for large-scale transformations and complex projects. In this episode Toby Mao explains how it works, the importance of automatic column-level lineage tracking, and how you can start using it today. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack- Your host is Tobias Macey and today I'm interviewing Toby Mao about SQLMesh, an open source DataOps framework designed to scale data transformations with ease of collaboration and validation built in Interview Introduction How did you get involved in the area of data management? Can you describe what SQLMesh is and the story behind it? DataOps is a term that has been co-opted and overloaded. What are the concepts that you are trying to convey with that term in the context of SQLMesh? What are the rough edges in existing toolchains/workflows that you are trying to address with SQLMesh? How do those rough edges impact the productivity and effectiveness of teams using those Can you describe how SQLMesh is implemented? How have the design and goals evolved since you first started working on it? What are the lessons that you have learned from dbt which have informed the design and functionality of SQLMesh? For teams who have already invested in dbt, what is the migration path from or integration with dbt? You have some built-in integration with/awareness of orchestrators (currently Airflow). What are the benefits of making the transformation tool aware of the orchestrator? What do you see as the potential benefits of integration with e.g. data-diff? What are the second-order benefits of using a tool such as SQLMesh that addresses the more mechanical aspects of managing transformation workfows and the associated dependency chains? What are the most interesting, innovative, or unexpected ways that you have seen SQLMesh used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLMesh? When is SQLMesh the wrong choice? What do you have planned for the future of SQLMesh? Contact Info tobymao on GitHub @captaintobs on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links SQLMesh Tobiko Data SAS AirBnB Minerva SQLGlot Cron AST == Abstract Syntax Tree Pandas Terraform dbt Podcast Episode SQLFluff Podcast.__init__ Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.Support Data Engineering Pod

Summary Architectural decisions are all based on certain constraints and a desire to optimize for different outcomes. In data systems one of the core architectural exercises is data modeling, which can have significant impacts on what is and is not possible for downstream use cases. By incorporating column-level lineage in the data modeling process it encourages a more robust and well-informed design. In this episode Satish Jayanthi explores the benefits of incorporating column-aware tooling in the data modeling process. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack- Your host is Tobias Macey and today I'm interviewing Satish Jayanthi about the practice and promise of building a column-aware data architecture through intentional modeling Interview Introduction How did you get involved in the area of data management? How has the move to the cloud for data warehousing/data platforms influenced the practice of data modeling? There are ongoing conversations about the continued merits of dimensional modeling techniques in modern warehouses. What are the modeling practices that you have found to be most useful in large and complex data environments? Can you describe what you mean by the term column-aware in the context of data modeling/data architecture? What are the capabilities that need to be built into a tool for it to be effectively column-aware? What are some of the ways that tools like dbt miss the mark in managing large/complex transformation workloads? Column-awareness is obviously critical in the context of the warehouse. What are some of the ways that that information can be fed into other contexts? (e.g. ML, reverse ETL, etc.) What is the importance of embedding column-level lineage awareness into transformation tool vs. layering on top w/ dedicated lineage/metadata tooling? What are the most interesting, innovative, or unexpected ways that you have seen column-aware data modeling used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on building column-aware tooling? When is column-aware modeling the wrong choice? What are some additional resources that you recommend for individuals/teams who want to learn more about data modeling/column aware principles? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Coalesce Podcast Episode Star Schema Conformed Dimensions Data Vault The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.Support Data Engineering Podcast

Summary Data engineering is all about building workflows, pipelines, systems, and interfaces to provide stable and reliable data. Your data can be stable and wrong, but then it isn't reliable. Confidence in your data is achieved through constant validation and testing. Datafold has invested a lot of time into integrating with the workflow of dbt projects to add early verification that the changes you are making are correct. In this episode Gleb Mezhanskiy shares some valuable advice and insights into how you can build reliable and well-tested data assets with dbt and data-diff. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy about how to test your dbt projects with Datafold Interview Introduction How did you get involved in the area of data management? Can you describe what Datafold is and what's new since we last spoke? (July 2021 and July 2022 about data-diff) What are the roadblocks to data testing/validation that you see teams run into most often? How does the tooling used contribute to/help address those roadblocks? What are some of the error conditions/failure modes that data-diff can help identify in a dbt project? What are some examples of tests that need to be implemented by the engineer? In your experience working with data teams, what typically constitutes the "staging area" for a dbt project? (e.g. separate warehouse, namespaced tables, snowflake data copies, lakefs, etc.) Given a dbt project that is well tested and has data-diff as part of the validation suite, what are the challenges that teams face in managing the feedback cycle of running those tests? In application development there is the idea of the "testing pyramid", consisting of unit tests, integration tests, system tests, etc. What are the parallels to that in data projects? What are the limitations of the data ecosystem that make testing a bigger challenge than it might otherwise be? Beyond test execution, what are the other aspects of data health that need to be included in the development and deployment workflow of dbt projects? (e.g. freshness, time to delivery, etc.) What are the most interesting, innovative, or unexpected ways that you have seen Datafold and/or data-diff used for testing dbt projects? What are the most interesting, unexpected, or challenging lessons that you have learned while working on dbt testing internally or with your customers? When is Datafold/data-diff the wrong choice for dbt projects? What do you have planned for the future of Datafold? Contact Info LinkedIn Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Datafold Podcast Episode data-diff Podcast Episode dbt Dagster dbt-cloud slim CI GitHub Actions Jenkins Circle CI Dolt Malloy LakeFS Planetscale Snowflake Zero Copy Cloning The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASpecial Guest: Gleb Mezhanskiy.Sponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about

Summary A significant portion of the time spent by data engineering teams is on managing the workflows and operations of their pipelines. DataOps has arisen as a parallel set of practices to that of DevOps teams as a means of reducing wasted effort. Agile Data Engine is a platform designed to handle the infrastructure side of the DataOps equation, as well as providing the insights that you need to manage the human side of the workflow. In this episode Tevje Olin explains how the platform is implemented, the features that it provides to reduce the amount of effort required to keep your pipelines running, and how you can start using it in your own team. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Tevje Olin about Agile Data Engine, a platform that combines data modeling, transformations, continuous delivery and workload orchestration to help you manage your data products and the whole lifecycle of your warehouse Interview Introduction How did you get involved in the area of data management? Can you describe what Agile Data Engine is and the story behind it? What are some of the tools and architectures that an organization might be able to replace with Agile Data Engine? How does the unified experience of Agile Data Engine change the way that teams think about the lifecycle of their data? What are some of the types of experiments that are enabled by reduced operational overhead? What does CI/CD look like for a data warehouse? How is it different from CI/CD for software applications? Can you describe how Agile Data Engine is architected? How have the design and goals of the system changed since you first started working on it? What are the components that you needed to develop in-house to enable your platform goals? What are the changes in the broader data ecosystem that have had the most influence on your product goals and customer adoption? Can you describe the workflow for a team that is using Agile Data Engine to power their business analytics? What are some of the insights that you generate to help your customers understand how to improve their processes or identify new opportunities? In your "about" page it mentions the unique approaches that you take for warehouse automation. How do your practices differ from the rest of the industry? How have changes in the adoption/implementation of ML and AI impacted the ways that your customers exercise your platform? What are the most interesting, innovative, or unexpected ways that you have seen the Agile Data Engine platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Agile Data Engine? When is Agile Data Engine the wrong choice? What do you have planned for the future of Agile Data Engine? Guest Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? About Agile Data Engine Agile Data Engine unlocks the potential of your data to drive business value - in a rapidly changing world. Agile Data Engine is a DataOps Management platform for designing, deploying, operating and managing data products, and managing the whole lifecycle of a data warehouse. It combines data modeling, transformations, continuous delivery and workload orchestration into the same platform. Links Agile Data Engine Bill Inmon Ralph Kimball Snowflake Redshift BigQuery Azure Synapse Airflow The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodca

Summary Building a data team is hard in any circumstance, but at a startup it can be even more challenging. The requirements are fluid, you probably don't have a lot of existing data talent to manage the hiring and onboarding, and there is a need to move fast. Ghalib Suleiman has been on both sides of this equation and joins the show to share his hard-won wisdom about how to start and grow a data team in the early days of company growth. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Ghalib Suleiman about challenges and strategies for building data teams in a startup Interview Introduction How did you get involved in the area of data management? Can you start by sharing your conception of the responsibilities of a data team? What are some of the common fallacies that organizations fall prey to in their first efforts at building data capabilities? Have you found it more practical to hire outside talent to build out the first data systems, or grow that talent internally? What are some of the resources you have found most helpful in training/educating the early creators and consumers of data assets? When there is no internal data talent to assist with hiring, what are some of the problems that manifest in the hiring process? What are the concepts that the new hire needs to know? How much does the hiring manager/interviewer need to know about those concepts to evaluate skill? What are the most critical skills for a first hire to have to start generating valuable output? As a solo data person, what are the uphill battles that they need to be prepared for in the organization? What are the rabbit holes that they should beware of? What are some of the tactical What are the most interesting, innovative, or unexpected ways that you have seen initial data hires tackle startup challenges? What are the most interesting, unexpected, or challenging lessons that you have learned while working on starting and growing data teams? When is it more practical to outsource the data work? Contact Info LinkedIn @ghalib on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Polytomic The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.Support Data Engineering Podcast

Summary Batch vs. streaming is a long running debate in the world of data integration and transformation. Proponents of the streaming paradigm argue that stream processing engines can easily handle batched workloads, but the reverse isn't true. The batch world has been the default for years because of the complexities of running a reliable streaming system at scale. In order to remove that barrier, the team at Estuary have built the Gazette and Flow systems from the ground up to resolve the pain points of other streaming engines, while providing an intuitive interface for data and application engineers to build their streaming workflows. In this episode David Yaffe and Johnny Graettinger share the story behind the business and technology and how you can start using it today to build a real-time data lake without all of the headache. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing David Yaffe and Johnny Graettinger about using streaming data to build a real-time data lake and how Estuary gives you a single path to integrating and transforming your various sources Interview Introduction How did you get involved in the area of data management? Can you describe what Estuary is and the story behind it? Stream processing technologies have been around for around a decade. How would you characterize the current state of the ecosystem? What was missing in the ecosystem of streaming engines that motivated you to create a new one from scratch? With the growth in tools that are focused on batch-oriented data integration and transformation, what are the reasons that an organization should still invest in streaming? What is the comparative level of difficulty and support for these disparate paradigms? What is the impact of continuous data flows on dags/orchestration of transforms? What role do modern table formats have on the viability of real-time data lakes? Can you describe the architecture of your Flow platform? What are the core capabilities that you are optimizing for in its design? What is involved in getting Flow/Estuary deployed and integrated with an organization's data systems? What does the workflow look like for a team using Estuary? How does it impact the overall system architecture for a data platform as compared to other prevalent paradigms? How do you manage the translation of poll vs. push availability and best practices for API and other non-CDC sources? What are the most interesting, innovative, or unexpected ways that you have seen Estuary used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Estuary? When is Estuary the wrong choice? What do you have planned for the future of Estuary? Contact Info Dave Y Johnny G Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Estuary Try Flow Free Gazette Samza Flink Podcast Episode Storm Kafka Topic Partitioning Trino Avro Parquet Fivetran Podcast Episode Airbyte Snowflake BigQuery Vector Database CDC == Change Data Capture Debezium Podcast Episode MapReduce Netflix DBLog JSON-Schema The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to buil

Summary All of the advancements in our technology is based around the principles of abstraction. These are valuable until they break down, which is an inevitable occurrence. In this episode the host Tobias Macey shares his reflections on recent experiences where the abstractions leaked and some observances on how to deal with that situation in a data platform architecture. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm sharing some thoughts and observances about abstractions and impedance mismatches from my experience building a data lakehouse with an ELT workflow Interview Introduction impact of community tech debt hive metastore new work being done but not widely adopted tensions between automation and correctness data type mapping integer types complex types naming things (keys/column names from APIs to databases) disaggregated databases - pros and cons flexibility and cost control not as much tooling invested vs. Snowflake/BigQuery/Redshift data modeling dimensional modeling vs. answering today's questions What are the most interesting, unexpected, or challenging lessons that you have learned while working on your data platform? When is ELT the wrong choice? What do you have planned for the future of your data platform? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links dbt Airbyte Podcast Episode Dagster Podcast Episode Trino Podcast Episode ELT Data Lakehouse Snowflake BigQuery Redshift Technical Debt Hive Metastore AWS Glue The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.Support Data Engineering Podcast

Summary Every business has customers, and a critical element of success is understanding who they are and how they are using the companies products or services. The challenge is that most companies have a multitude of systems that contain fragments of the customer's interactions and stitching that together is complex and time consuming. Segment created the Unify product to reduce the burden of building a comprehensive view of customers and synchronizing it to all of the systems that need it. In this episode Kevin Niparko and Hanhan Wang share the details of how it is implemented and how you can use it to build and maintain rich customer profiles. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Kevin Niparko and Hanhan Wang about Segment's new Unify product for building and syncing comprehensive customer profiles across your data systems Interview Introduction How did you get involved in the area of data management? Can you describe what Segment Unify is and the story behind it? What are the net-new capabilities that it brings to the Segment product suite? What are some of the categories of attributes that need to be managed in a prototypical customer profile? What are the different use cases that are enabled/simplified by the availability of a comprehensive customer profile? What is the potential impact of more detailed customer profiles on LTV? How do you manage permissions/auditability of updating or amending profile data? Can you describe how the Unify product is implemented? What are the technical challenges that you had to address while developing/launching this product? What is the workflow for a team who is adopting the Unify product? What are the other Segment products that need to be in use to take advantage of Unify? What are some of the most complex edge cases to address in identity resolution? How does reverse ETL factor into the enrichment process for profile data? What are some of the issues that you have to account for in synchronizing profiles across platforms/products? How do you mititgate the impact of "regression to the mean" for systems that don't support all of the attributes that you want to maintain in a profile record? What are some of the data modeling considerations that you have had to account for to support e.g. historical changes (e.g. slowly changing dimensions)? What are the most interesting, innovative, or unexpected ways that you have seen Segment Unify used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Segment Unify? When is Segment Unify the wrong choice? What do you have planned for the future of Segment Unify? Contact Info Kevin LinkedIn Blog Hanhan LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Segment Unify Segment Podcast Episode Customer Data Platform (CDP) Golden Profile Reverse ETL MarTech Landscape The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events

Summary Real-time capabilities have quickly become an expectation for consumers. The complexity of providing those capabilities is still high, however, making it more difficult for small teams to compete. Meroxa was created to enable teams of all sizes to deliver real-time data applications. In this episode DeVaris Brown discusses the types of applications that are possible when teams don't have to manage the complex infrastructure necessary to support continuous data flows. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing DeVaris Brown about the impact of real-time data on business opportunities and risk profiles Interview Introduction How did you get involved in the area of data management? Can you describe what Meroxa is and the story behind it? How have the focus and goals of the platform and company evolved over the past 2 years? Who are the target customers for Meroxa? What problems are they trying to solve when they come to your platform? Applications powered by real-time data were the exclusive domain of large and/or sophisticated tech companies for several years due to the inherent complexities involved. What are the shifts that have made them more accessible to a wider variety of teams? What are some of the remaining blockers for teams who want to start using real-time data? With the democratization of real-time data, what are the new categories of products and applications that are being unlocked? How are organizations thinking about the potential value that those types of apps/services can provide? With data flowing constantly, there are new challenges around oversight and accuracy. How does real-time data change the risk profile for applications that are consuming it? What are some of the technical controls that are available for organizations that are risk-averse? What skills do developers need to be able to effectively design, develop, and deploy real-time data applications? How does this differ when talking about internal vs. consumer/end-user facing applications? What are the most interesting, innovative, or unexpected ways that you have seen Meroxa used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Meroxa? When is Meroxa the wrong choice? What do you have planned for the future of Meroxa? Contact Info LinkedIn @devarispbrown on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Meroxa Podcast Episode Kafka Kafka Connect Conduit - golang Kafka connect replacement Pulsar Redpanda Flink Beam Clickhouse Druid Pinot The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.Support Data Engineering Podcast

Summary Business intellingence has been chasing the promise of self-serve data for decades. As the capabilities of these systems has improved and become more accessible, the target of what self-serve means changes. With the availability of AI powered by large language models combined with the evolution of semantic layers, the team at Zenlytic have taken aim at this problem again. In this episode Paul Blankley and Ryan Janssen explore the power of natural language driven data exploration combined with semantic modeling that enables an intuitive way for everyone in the business to access the data that they need to succeed in their work. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Paul Blankley and Ryan Janssen about Zenlytic, a no-code business intelligence tool focused on emerging commerce brands Interview Introduction How did you get involved in the area of data management? Can you describe what Zenlytic is and the story behind it? Business intelligence is a crowded market. What was your process for defining the problem you are focused on solving and the method to achieve that outcome? Self-serve data exploration has been attempted in myriad ways over successive generations of BI and data platforms. What are the barriers that have been the most challenging to overcome in that effort? What are the elements that are coming together now that give you confidence in being able to deliver on that? Can you describe how Zenlytic is implemented? What are the evolutions in the understanding and implementation of semantic layers that provide a sufficient substrate for operating on? How have the recent breakthroughs in large language models (LLMs) improved your ability to build features in Zenlytic? What is your process for adding domain semantics to the operational aspect of your LLM? For someone using Zenlytic, what is the process for getting it set up and integrated with their data? Once it is operational, can you describe some typical workflows for using Zenlytic in a business context? Who are the target users? What are the collaboration options available? What are the most complex engineering/data challenges that you have had to address in building Zenlytic? What are the most interesting, innovative, or unexpected ways that you have seen Zenlytic used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Zenlytic? When is Zenlytic the wrong choice? What do you have planned for the future of Zenlytic? Contact Info Paul Blankley (LinkedIn) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Zenlytic OLAP Cube Large Language Model Starburst Prompt Engineering ChatGPT The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and

Summary The customer data platform is a category of services that was developed early in the evolution of the current era of cloud services for data processing. When it was difficult to wire together the event collection, data modeling, reporting, and activation it made sense to buy monolithic products that handled every stage of the customer data lifecycle. Now that the data warehouse has taken center stage a new approach of composable customer data platforms is emerging. In this episode Darren Haken is joined by Tejas Manohar to discuss how Autotrader UK is addressing their customer data needs by building on top of their existing data stack. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Darren Haken and Tejas Manohar about building a composable CDP and how you can start adopting it incrementally Interview Introduction How did you get involved in the area of data management? Can you describe what you mean by a "composable CDP"? What are some of the key ways that it differs from the ways that we think of a CDP today? What are the problems that you were focused on addressing at Autotrader that are solved by a CDP? One of the promises of the first generation CDP was an opinionated way to model your data so that non-technical teams could own this responsibility. What do you see as the risks/tradeoffs of moving CDP functionality into the same data stack as the rest of the organization? What about companies that don't have the capacity to run a full data infrastructure? Beyond the core technology of the data warehouse, what are the other evolutions/innovations that allow for a CDP experience to be built on top of the core data stack? added burden on core data teams to generate event-driven data models When iterating toward a CDP on top of the core investment of the infrastructure to feed and manage a data warehouse, what are the typical first steps? What are some of the components in the ecosystem that help to speed up the time to adoption? (e.g. pre-built dbt packages for common transformations, etc.) What are the most interesting, innovative, or unexpected ways that you have seen CDPs implemented? What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDP related functionality? When is a CDP (composable or monolithic) the wrong choice? What do you have planned for the future of the CDP stack? Contact Info Darren LinkedIn @DarrenHaken on Twitter Tejas LinkedIn @tejasmanohar on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Autotrader Hightouch Customer Studio CDP == Customer Data Platform Segment Podcast Episode mParticle Salesforce Amplitude Snowplow Podcast Episode Reverse ETL dbt Podcast Episode Snowflake Podcast Episode BigQuery Databricks ELT Fivetran Podcast Episode DataHub Podcast Episode Amundsen Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools,

Summary The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process Interview Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it? At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape? As the data landscape matures, how have you seen that influence the types of projects/companies that are founded? What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive? The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve? Is there a different architectural paradigm that you see as growing to take its place? How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape? Contact Info Website @mattturck on Twitter MAD Landscape Comments Email Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links MAD Landscape First Mark Capital Bayesian Learning AI Winter Databricks Cloud Native Landscape LUMA Scape Hadoop Ecosystem Modern Data Stack Reverse ETL Generative AI dbt Transform Podcast Episode Snowflake IPO Dataiku Iceberg Podcast Episode Hudi Podcast Episode DuckDB Podcast Episode Trino Y42 Podcast Episode Mozart Data Podcast Episode Keboola MPP Database The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Rudderstack:  Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit [RudderStack.com/DEP](https://rudderstack.com/dep) to learn moreSupport Data Engineering Podcast

Summary The promise of streaming data is that it allows you to react to new information as it happens, rather than introducing latency by batching records together. The peril is that building a robust and scalable streaming architecture is always more complicated and error-prone than you think it's going to be. After experiencing this unfortunate reality for themselves, Abhishek Chauhan and Ashish Kumar founded Grainite so that you don't have to suffer the same pain. In this episode they explain why streaming architectures are so challenging, how they have designed Grainite to be robust and scalable, and how you can start using it today to build your streaming data applications without all of the operational headache. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender where you can do two things: watch us build a data estate in 15 minutes and start for free today. Join in with the event for the global data community, Data Council Austin. From March 28-30th 2023, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council today Your host is Tobias Macey and today I'm interviewing Ashish Kumar and Abhishek Chauhan about Grainite, a platform designed to give you a single place to build streaming data applications Interview Introduction How did you get involved in the area of data management? Can you describe what Grainite is and the story behind it? What are the personas that you are focused on addressing with Grainite? What are some of the most complex aspects of building streaming data applications in the absence of something like Grainite? How does Grainite work to reduce that complexity? What are some of the commonalities that you see in the teams/organizations that find their way to Grainite? What are some of the higher-order projects that teams are able to build when they are using Grainite as a starting point vs. where they would be spending effort on a fully managed streaming architecture? Can you describe how Grainite is architected? How have the design and goals of the platform changed/evolved since you first started working on it? What does your internal build vs. buy process look like for identifying where to spend your engineering resources? What is the process for getting Grainite set up and integrated into an organizations technical environment? What is your process for determining which elements of the platform to expose as end-user features and customization options vs. keeping internal to the operational aspects of the product? Once Grainite is running, can you describe the day 0 workflow of building an application or data flow? What are the day 2 - N capabilities that Grainite offers for ongoing maintenance/operation/evolution of those applications? What are the most interesting, innovative, or unexpected ways that you have seen Grainite used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Grainite? When is Grainite the wrong choice? What do you have planned for the future of Grainite? Contact Info Ashish LinkedIn Abhish

Summary As with all aspects of technology, security is a critical element of data applications, and the different controls can be at cross purposes with productivity. In this episode Yoav Cohen from Satori shares his experiences as a practitioner in the space of data security and how to align with the needs of engineers and business users. He also explains why data security is distinct from application security and some methods for reducing the challenge of working across different data systems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Join in with the event for the global data community, Data Council Austin. From March 28-30th 2023, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council today RudderStack makes it easy for data teams to build a customer data platform on their own warehouse. Use their state of the art pipelines to collect all of your data, build a complete view of your customer and sync it to every downstream tool. Sign up for free at dataengineeringpodcast.com/rudder Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender where you can do two things: watch us build a data estate in 15 minutes and start for free today. Your host is Tobias Macey and today I'm interviewing Yoav Cohen about the challenges that data teams face in securing their data platforms and how that impacts the productivity and adoption of data in the organization Interview Introduction How did you get involved in the area of data management? Data security is a very broad term. Can you start by enumerating some of the different concerns that are involved? How has the scope and complexity of implementing security controls on data systems changed in recent years? In your experience, what is a typical number of data locations that an organization is trying to manage access/permissions within? What are some of the main challenges that data/compliance teams face in establishing and maintaining security controls? How much of the problem is technical vs. procedural/organizational? As a vendor in the space, how do you think about the broad categories/boundary lines for the different elements of data security? (e.g. masking vs. RBAC, etc.) What are the different layers that are best suited to managing each of those categories? (e.g. masking and encryption in storage layer, RBAC in warehouse, etc.) What are some of the ways that data security and organizational productivity are at odds with each other? What are some of the shortcuts that you see teams and individuals taking to address the productivity hit from security controls? What are some of the methods that you have found to be most effective at mitigating or even improving productivity impacts through security controls? How does up-front design of the security layers improve the final outcome vs. trying to bolt on security after the platform is already in use? How can education about the motivations for different security practices improve compliance and user experience? What are the most interesting, innovative, or unexpected ways that you have seen data teams align data security and productivity? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data security technology? What are the areas of data security that still need improvements? Contact Info Yoav Cohen Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the inno

Summary With the rise of the web and digital business came the need to understand how customers are interacting with the products and services that are being sold. Product analytics has grown into its own category and brought with it several services with generational differences in how they approach the problem. NetSpring is a warehouse-native product analytics service that allows you to gain powerful insights into your customers and their needs by combining your event streams with the rest of your business data. In this episode Priyendra Deshwal explains how NetSpring is designed to empower your product and data teams to build and explore insights around your products in a streamlined and maintainable workflow. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Join in with the event for the global data community, Data Council Austin. From March 28-30th 2023, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council today! RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Your host is Tobias Macey and today I'm interviewing Priyendra Deshwal about how NetSpring is using the data warehouse to deliver a more flexible and detailed view of your product analytics Interview Introduction How did you get involved in the area of data management? Can you describe what NetSpring is and the story behind it? What are the activities that constitute "product analytics" and what are the roles/teams involved in those activities? When teams first come to you, what are the common challenges that they are facing and what are the solutions that they have attempted to employ? Can you describe some of the challenges involved in bringing product analytics into enterprise or highly regulated environments/industries? How does a warehouse-native approach simplify that effort? There are many different players (both commercial and open source) in the product analytics space. Can you share your view on the role that NetSpring plays in that ecosystem? How is the NetSpring platform implemented to be able to best take advantage of modern warehouse technologies and the associated data stacks? What are the pre-requisites for an organization's infrastructure/data maturity for being able to benefit from NetSpring? How have the goals and implementation of the NetSpring platform evolved from when you first started working on it? Can you describe the steps involved in integrating NetSpring with an organization's existing warehouse? What are the signals that NetSpring uses to understand the customer journeys of different organizations? How do you manage the variance of the data models in the warehouse while providing a consistent experience for your users? Given that you are a product organization, how are you using NetSpring to power NetSpring? What are the most interesting, innovative, or unexpected ways that you have seen NetSpring used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on NetSpring? When is NetSpring the wrong choice? What do you have planned for the future of NetSpring? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links NetSpring ThoughtSpot Product Analytics Amplitude Mixpanel Customer Data Platform GDPR CCPA Segment Podca

Summary The ecosystem for data professionals has matured to the point that there are a large and growing number of distinct roles. With the scope and importance of data steadily increasing it is important for organizations to ensure that everyone is aligned and operating in a positive environment. To help facilitate the nascent conversation about what constitutes an effective and productive data culture, the team at Data Council have dedicated an entire conference track to the subject. In this episode Pete Soderling and Maggie Hays join the show to explore this topic and their experience preparing for the upcoming conference. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender where you can do two things: watch us build a data estate in 15 minutes and start for free today. Your host is Tobias Macey and today I'm interviewing Pete Soderling and Maggie Hays about the growing importance of establishing and investing in an organization's data culture and their experience forming an entire conference track around this topic Interview Introduction How did you get involved in the area of data management? Can you describe what your working definition of "Data Culture" is? In what ways is a data culture distinct from an organization's corporate culture? How are they interdependent? What are the elements that are most impactful in forming the data culture of an organization? What are some of the motivations that teams/companies might have in fighting against the creation and support of an explicit data culture? Are there any strategies that you have found helpful in counteracting those tendencies? In terms of the conference, what are the factors that you consider when deciding how to group the different presentations into tracks or themes? What are the experiences that you have had personally and in community interactions that led you to elevate data culture to be it's own track? What are the broad challenges that practitioners are facing as they develop their own understanding of what constitutes a healthy and productive data culture? What are some of the risks that you considered when forming this track and evaluating proposals? What are your criteria for determining whether this track is successful? What are the most interesting, innovative, or unexpected aspects of data culture that you have encountered through developing this track? What are the most interesting, unexpected, or challenging lessons that you have learned while working on selecting presentations for this year's event? What do you have planned for the future of this topic at Data Council events? Contact Info Pete @petesoder on Twitter LinkedIn Maggie LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Data Council Podcast Episode Data Community Fund DataHub Podcast Episode Database Design For Mere Mortals by Michael J. Hernandez (affiliate link) SOAP REST Econometrics DBA == Database Administrator Conway's Law dbt Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:TimeXtender:  TimeXtender is a holistic, metadata-driven solution for data integration, optimized for agility. TimeXtender provides all the f

Summary There has been a lot of discussion about the practical application of data mesh and how to implement it in an organization. Jean-Georges Perrin was tasked with designing a new data platform implementation at PayPal and wound up building a data mesh. In this episode he shares that journey and the combination of technical and organizational challenges that he encountered in the process. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender where you can do two things: watch us build a data estate in 15 minutes and start for free today. Your host is Tobias Macey and today I'm interviewing Jean-Georges Perrin about his work at PayPal to implement a data mesh and the role of data contracts in making it work Interview Introduction How did you get involved in the area of data management? Can you start by describing the goals and scope of your work at PayPal to implement a data mesh? What are the core problems that you were addressing with this project? Is a data mesh ever "done"? What was your experience engaging at the organizational level to identify the granularity and ownership of the data products that were needed in the initial iteration? What was the impact of leading multiple teams on the design of how to implement communication/contracts throughout the mesh? What are the technical systems that you are relying on to power the different data domains? What is your philosophy on enforcing uniformity in technical systems vs. relying on interface definitions as the unit of consistency? What are the biggest challenges (technical and procedural) that you have encountered during your implementation? How are you managing visibility/auditability across the different data domains? (e.g. observability, data quality, etc.) What are the most interesting, innovative, or unexpected ways that you have seen PayPal's data mesh used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh? When is a data mesh the wrong choice? What do you have planned for the future of your data mesh at PayPal? Contact Info LinkedIn Blog Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Data Mesh O'Reilly Book (affiliate link) The next generation of Data Platforms is the Data Mesh PayPal Conway's Law Data Mesh For All Ages - US, Data Mesh For All Ages - UK Data Mesh Radio Data Mesh Community Data Mesh In Action Great Expectations Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:TimeXtender:  TimeXtender is a holistic, metadata-driven solution for data integration, optimized for agility. TimeXtender provides all the features you need to build a future-proof infrastructure for ingesting, transforming, modelling, and delivering clean, reliable data in the fastest, most efficient way possible. You can't optimize for everything all at once. That's why we take a holistic approach to data integration that optimises for agility instead of fragmentation. By unifying each layer of the data stack, TimeXtender empowers you to build data solutions 10x faster while reducing costs by 70%-80%. We do this for one simple reason: because time matters. Go to [dataengineeringpodcast.com/timext

Summary Cloud data warehouses have unlocked a massive amount of innovation and investment in data applications, but they are still inherently limiting. Because of their complete ownership of your data they constrain the possibilities of what data you can store and how it can be used. Projects like Apache Iceberg provide a viable alternative in the form of data lakehouses that provide the scalability and flexibility of data lakes, combined with the ease of use and performance of data warehouses. Ryan Blue helped create the Iceberg project, and in this episode he rejoins the show to discuss how it has evolved and what he is doing in his new business Tabular to make it even easier to implement and maintain. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to timextender.com/dataengineering where you can do two things: watch us build a data estate in 15 minutes and start for free today. Your host is Tobias Macey and today I'm interviewing Ryan Blue about the evolution and applications of the Iceberg table format and how he is making it more accessible at Tabular Interview Introduction How did you get involved in the area of data management? Can you describe what Iceberg is and its position in the data lake/lakehouse ecosystem? Since it is a fundamentally a specification, how do you manage compatibility and consistency across implementations? What are the notable changes in the Iceberg project and its role in the ecosystem since our last conversation October of 2018? Around the time that Iceberg was first created at Netflix a number of alternative table formats were also being developed. What are the characteristics of Iceberg that lead teams to adopt it for their lakehouse projects? Given the constant evolution of the various table formats it can be difficult to determine an up-to-date comparison of their features, particularly earlier in their development. What are the aspects of this problem space that make it so challenging to establish unbiased and comprehensive comparisons? For someone who wants to manage their data in Iceberg tables, what does the implementation look like? How does that change based on the type of query/processing engine being used? Once a table has been created, what are the capabilities of Iceberg that help to support ongoing use and maintenance? What are the most interesting, innovative, or unexpected ways that you have seen Iceberg used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Iceberg/Tabular? When is Iceberg/Tabular the wrong choice? What do you have planned for the future of Iceberg/Tabular? Contact Info LinkedIn rdblue on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Iceberg Podcast Episode Hadoop Data Lakehouse ACID == Atomic, Consistent, Isolated, Durable Apache Hive Apache Impala Bodo Podcast Episode StarRocks Dremio Podcast Episode DDL == Data Definition Language Trino PrestoDB Apache Hudi Podcast Episode dbt Apache Flink TileDB Podcast Episode CDC == Change Data Capture Substrait The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Acryl:  The modern data stack needs a reimagined metadata management platform. Acryl Data’s vision is to bring clarity to your da

Summary Data is a team sport, but it's often difficult for everyone on the team to participate. For a long time the mantra of data tools has been "by developers, for developers", which automatically excludes a large portion of the business members who play a crucial role in the success of any data project. Quilt Data was created as an answer to make it easier for everyone to contribute to the data being used by an organization and collaborate on its application. In this episode Aneesh Karve shares the journey that Quilt has taken to provide an approachable interface for working with versioned data in S3 that empowers everyone to collaborate. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions! Your host is Tobias Macey and today I'm interviewing Aneesh Karve about how Quilt Data helps you bring order to your chaotic data in S3 with transactional versioning and data discovery built in Interview Introduction How did you get involved in the area of data management? Can you describe what Quilt is and the story behind it? How have the goals and features of the Quilt platform changed since I spoke with Kevin in June of 2018? What are the main problems that users are trying to solve when they find Quilt? What are some of the alternative approaches/products that they are coming from? How does Quilt compare with options such as LakeFS, Unstruk, Pachyderm, etc.? Can you describe how Quilt is implemented? What are the types of tools and systems that Quilt gets integrated with? How do you manage the tension between supporting the lowest common denominator, while providing options for more advanced capabilities? What is a typical workflow for a team that is using Quilt to manage their data? What are the most interesting, innovative, or unexpected ways that you have seen Quilt used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Quilt? When is Quilt the wrong choice? What do you have planned for the future of Quilt? Contact Info LinkedIn @akarve on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Quilt Data Podcast Episode UW Madison Docker Swarm Kaggle open.quiltdata.com FinOS Perspective LakeFS Podcast Episode Pachyderm Podcast Episode Unstruk Podcast Episode Parquet Avro ORC Cloudformation Troposphere CDK == Cloud Development Kit Shadow IT Podcast Episode Delta Lake Podcast Episode Apache Iceberg Podcast Episode Datasette Frictionless DVC Podcast.__init__ Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Materialize:  Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use. Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features. Sign up now for early access to Mate

Summary This podcast started almost exactly six years ago, and the technology landscape was much different than it is now. In that time there have been a number of generational shifts in how data engineering is done. In this episode I reflect on some of the major themes and take a brief look forward at some of the upcoming changes. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm reflecting on the major trends in data engineering over the past 6 years Interview Introduction 6 years of running the Data Engineering Podcast Around the first time that data engineering was discussed as a role Followed on from hype about "data science" Hadoop era Streaming Lambda and Kappa architectures Not really referenced anymore "Big Data" era of capture everything has shifted to focusing on data that presents value Regulatory environment increases risk, better tools introduce more capability to understand what data is useful Data catalogs Amundsen and Alation Orchestration engine Oozie, etc. -> Airflow and Luigi -> Dagster, Prefect, Lyft, etc. Orchestration is now a part of most vertical tools Cloud data warehouses Data lakes DataOps and MLOps Data quality to data observability Metadata for everything Data catalog -> data discovery -> active metadata Business intelligence Read only reports to metric/semantic layers Embedded analytics and data APIs Rise of ELT dbt Corresponding introduction of reverse ETL What are the most interesting, unexpected, or challenging lessons that you have learned while working on running the podcast? What do you have planned for the future of the podcast? Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASponsored By:Materialize:  Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use. Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features. Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access)Support Data Engineering Podcast