Data Engineering Podcast

Summary Apache Spark is a popular and widely used tool for a variety of data oriented projects. With the large array of capabilities, and the complexity of the underlying system, it can be difficult to understand how to get started using it. Jean George Perrin has been so impressed by the versatility of Spark that he is writing a book for data engineers to hit the ground running. In this episode he helps to make sense of what Spark is, how it works, and the various ways that you can use it. He also discusses what you need to know to get it deployed and keep it running in a production environment and how it fits into the overall data ecosystem. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jean Georges Perrin, author of the upcoming Manning book Spark In Action 2nd Edition, about the ways that Spark is used and how it fits into the data landscape Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Spark is? What are some of the main use cases for Spark? What are some of the problems that Spark is uniquely suited to address? Who uses Spark? What are the tools offered to Spark users? How does it compare to some of the other streaming frameworks such as Flink, Kafka, or Storm? For someone building on top of Spark what are the main software design paradigms? How does the design of an application change as you go from a local development environment to a production cluster? Once your application is written, what is involved in deploying it to a production environment? What are some of the most useful strategies that you have seen for improving the efficiency and performance of a processing pipeline? What are some of the edge cases and architectural considerations that engineers should be considering as they begin to scale their deployments? What are some of the common ways that Spark is deployed, in terms of the cluster topology and the supporting technologies? What are the limitations of the Spark programming model? What are the cases where Spark is the wrong choice? What was your motivation for writing a book about Spark? Who is the target audience? What have been some of the most interesting or useful lessons that you have learned in the process of writing a book about Spark? What advice do you have for anyone who is considering or currently using Spark? Contact Info @jgperrin on Twitter Blog Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Book Discount Use the code poddataeng18 to get 40% off of all of Manning’s products at manning.com Links Apache Spark Spark In Action Book code examples in GitHub Informix International Informix Users Group MySQL Microsoft SQL Server ETL (Extract, Transform, Load) Spark SQL and Spark In Action‘s chapter 11 Spark ML and Spark In Action‘s chapter 18 Spark Streaming (structured) and Spark In Action‘s chapter 10 Spark GraphX Hadoop Jupyter Podcast Interview Zeppelin Databricks IBM Watson Studio Kafka Flink Podcast Episode AWS Kinesis Yarn HDFS Hive Scala PySpark DAG Spark Catalyst Spark Tungsten Spark UDF AWS EMR Mesos DC/OS Kubernetes Dataframes The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Distributed systems are complex to build and operate, and there are certain primitives that are common to a majority of them. Rather then re-implement the same capabilities every time, many projects build on top of Apache Zookeeper. In this episode Patrick Hunt explains how the Apache Zookeeper project was started, how it functions, and how it is used as a building block for other distributed systems. He also explains the operational considerations for running your own cluster, how it compares to more recent entrants such as Consul and EtcD, and what is in store for the future. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Patrick Hunt about Apache Zookeeper and how it is used as a building block for distributed systems Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Zookeeper is and how the project got started? What are the main motivations for using a centralized coordination service for distributed systems? What are the distributed systems primitives that are built into Zookeeper? What are some of the higher-order capabilities that Zookeeper provides to users who are building distributed systems on top of Zookeeper? What are some of the types of system level features that application developers will need which aren’t provided by Zookeeper? Can you discuss how Zookeeper is architected and how that design has evolved over time? What have you found to be some of the most complicated or difficult aspects of building and maintaining Zookeeper? What are the scaling factors for Zookeeper? What are the edge cases that users should be aware of? Where does it fall on the axes of the CAP theorem? What are the main failure modes for Zookeeper? How much of the recovery logic is left up to the end user of the Zookeeper cluster? Since there are a number of projects that rely on Zookeeper, many of which are likely to be run in the same environment (e.g. Kafka and Flink), what would be involved in sharing a single Zookeeper cluster among those multiple services? In recent years we have seen projects such as EtcD which is used by Kubernetes, and Consul. How does Zookeeper compare with those projects? What are some of the cases where Zookeeper is the wrong choice? How have the needs of distributed systems engineers changed since you first began working on Zookeeper? If you were to start the project over today, what would you do differently? Would you still use Java? What are some of the most interesting or unexpected ways that you have seen Zookeeper used? What do you have planned for the future of Zookeeper? Contact Info @phunt on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Zookeeper Cloudera Google Chubby Sourceforge HBase High Availability Fallacies of distributed computing Falsehoods programmers believe about networking Consul EtcD Apache Curator Raft Consensus Algorithm Zookeeper Atomic Broadcast SSD Write Cliff Apache Kafka Apache Flink Podcast Episode HDFS Kubernetes Netty Protocol Buffers Avro Rust The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary When your data lives in multiple locations, belonging to at least as many applications, it is exceedingly difficult to ask complex questions of it. The default way to manage this situation is by crafting pipelines that will extract the data from source systems and load it into a data lake or data warehouse. In order to make this situation more manageable and allow everyone in the business to gain value from the data the folks at Dremio built a self service data platform. In this episode Tomer Shiran, CEO and co-founder of Dremio, explains how it fits into the modern data landscape, how it works under the hood, and how you can start using it today to make your life easier. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Tomer Shiran about Dremio, the open source data as a service platform Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Dremio is and how the project and business got started? What was the motivation for keeping your primary product open source? What is the governance model for the project? How does Dremio fit in the current landscape of data tools? What are some use cases that Dremio is uniquely equipped to support? Do you think that Dremio obviates the need for a data warehouse or large scale data lake? How is Dremio architected internally? How has that architecture evolved from when it was first built? There are a large array of components (e.g. governance, lineage, catalog) built into Dremio that are often found in dedicated products. What are some of the strategies that you have as a business and development team to manage and integrate the complexity of the product? What are the benefits of integrating all of those capabilities into a single system? What are the drawbacks? One of the useful features of Dremio is the granular access controls. Can you discuss how those are implemented and controlled? For someone who is interested in deploying Dremio to their environment what is involved in getting it installed? What are the scaling factors? What are some of the most exciting features that have been added in recent releases? When is Dremio the wrong choice? What have been some of the most challenging aspects of building, maintaining, and growing the technical and business platform of Dremio? What do you have planned for the future of Dremio? Contact Info Tomer @tshiran on Twitter LinkedIn Dremio Website @dremio on Twitter dremio on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Dremio MapR Presto Business Intelligence Arrow Tableau Power BI Jupyter OLAP Cube Apache Foundation Hadoop Nikon DSLR Spark ETL (Extract, Transform, Load) Parquet Avro K8s Helm Yarn Gandiva Initiative for Apache Arrow LLVM TLS The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Modern applications and data platforms aspire to process events and data in real time at scale and with low latency. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. In this episode Fabian Hueske, one of the original authors, explains how Flink is architected, how it is being used to power some of the world’s largest businesses, where it sits in the lanscape of stream processing tools, and how you can start using it today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Fabian Hueske, co-author of the upcoming O’Reilly book Stream Processing With Apache Flink, about his work on Apache Flink, the stateful streaming engine Interview Introduction How did you get involved in the area of data management? Can you start by describing what Flink is and how the project got started? What are some of the primary ways that Flink is used? How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm? What are some use cases that Flink is uniquely qualified to handle? Where does Flink fit into the current data landscape? How is Flink architected? How has that architecture evolved? Are there any aspects of the current design that you would do differently if you started over today? How does scaling work in a Flink deployment? What are the scaling limits? What are some of the failure modes that users should be aware of? How is the statefulness of a cluster managed? What are the mechanisms for managing conflicts? What are the limiting factors for the volume of state that can be practically handled in a cluster and for a given purpose? Can state be shared across processes or tasks within a Flink cluster? What are the comparative challenges of working with bounded vs unbounded streams of data? How do you handle out of order events in Flink, especially as the delay for a given event increases? For someone who is using Flink in their environment, what are the primary means of interacting with and developing on top of it? What are some of the most challenging or complicated aspects of building and maintaining Flink? What are some of the most interesting or unexpected ways that you have seen Flink used? What are some of the improvements or new features that are planned for the future of Flink? What are some features or use cases that you are explicitly not planning to support? For people who participate in the training sessions that you offer through Data Artisans, what are some of the concepts that they are challenged by? What do they find most interesting or exciting? Contact Info LinkedIn @fhueske on Twitter fhueske on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Flink Data Artisans IBM DB2 Technische Universität Berlin Hadoop Relational Database Google Cloud Dataflow Spark Cascading Java RocksDB Flink Checkpoints Flink Savepoints Kafka Pulsar Storm Scala LINQ (Language INtegrated Query) SQL Backpressure Watermarks HDFS S3 Avro JSON Hive Metastore Dell EMC Pravega The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary A data lake can be a highly valuable resource, as long as it is well built and well managed. Unfortunately, that can be a complex and time-consuming effort, requiring specialized knowledge and diverting resources from your primary business. In this episode Yoni Iny, CTO of Upsolver, discusses the various components that are necessary for a successful data lake project, how the Upsolver platform is architected, and how modern data lakes can benefit your organization. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Yoni Iny about Upsolver, a data lake platform that lets developers integrate and analyze streaming data with ease Interview Introduction How did you get involved in the area of data management? Can you start by describing what Upsolver is and how it got started? What are your goals for the platform? There are a lot of opinions on both sides of the data lake argument. When is it the right choice for a data platform? What are the shortcomings of a data lake architecture? How is Upsolver architected? How has that architecture changed over time? How do you manage schema validation for incoming data? What would you do differently if you were to start over today? What are the biggest challenges at each of the major stages of the data lake? What is the workflow for a user of Upsolver and how does it compare to a self-managed data lake? When is Upsolver the wrong choice for an organization considering implementation of a data platform? Is there a particular scale or level of data maturity for an organization at which they would be better served by moving management of their data lake in house? What features or improvements do you have planned for the future of Upsolver? Contact Info Yoni yoniiny on GitHub LinkedIn Upsolver Website @upsolver on Twitter LinkedIn Facebook Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Upsolver Data Lake Israeli Army Data Warehouse Data Engineering Podcast Episode About Data Curation Three Vs Kafka Spark Presto Drill Spot Instances Object Storage Cassandra Redis Latency Avro Parquet ORC Data Engineering Podcast Episode About Data Serialization Formats SSTables Run Length Encoding CSV (Comma Separated Values) Protocol Buffers Kinesis ETL DevOps Prometheus Cloudwatch DataDog InfluxDB SQL Pandas Confluent KSQL The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Business intelligence is a necessity for any organization that wants to be able to make informed decisions based on the data that they collect. Unfortunately, it is common for different portions of the business to build their reports with different assumptions, leading to conflicting views and poor choices. Looker is a modern tool for building and sharing reports that makes it easy to get everyone on the same page. In this episode Daniel Mintz explains how the product is architected, the features that make it easy for any business user to access and explore their reports, and how you can use it for your organization today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Daniel Mintz about Looker, a a modern data platform that can serve the data needs of an entire company Interview Introduction How did you get involved in the area of data management? Can you start by describing what Looker is and the problem that it is aiming to solve? How do you define business intelligence? How is Looker unique from other approaches to business intelligence in the enterprise? How does it compare to open source platforms for BI? Can you describe the technical infrastructure that supports Looker? Given that you are connecting to the customer’s data store, how do you ensure sufficient security? For someone who is using Looker, what does their workflow look like? How does that change for different user roles (e.g. data engineer vs sales management) What are the scaling factors for Looker, both in terms of volume of data for reporting from, and for user concurrency? What are the most challenging aspects of building a business intelligence tool and company in the modern data ecosystem? What are the portions of the Looker architecture that you would do differently if you were to start over today? What are some of the most interesting or unusual uses of Looker that you have seen? What is in store for the future of Looker? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Looker Upworthy MoveOn.org LookML SQL Business Intelligence Data Warehouse Linux Hadoop BigQuery Snowflake Redshift DB2 PostGres ETL (Extract, Transform, Load) ELT (Extract, Load, Transform) Airflow Luigi NiFi Data Curation Episode Presto Hive Athena DRY (Don’t Repeat Yourself) Looker Action Hub Salesforce Marketo Twilio Netscape Navigator Dynamic Pricing Survival Analysis DevOps BigQuery ML Snowflake Data Sharehouse The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Jupyter notebooks have gained popularity among data scientists as an easy way to do exploratory analysis and build interactive reports. However, this can cause difficulties when trying to move the work of the data scientist into a more standard production environment, due to the translation efforts that are necessary. At Netflix they had the crazy idea that perhaps that last step isn’t necessary, and the production workflows can just run the notebooks directly. Matthew Seal is one of the primary engineers who has been tasked with building the tools and practices that allow the various data oriented roles to unify their work around notebooks. In this episode he explains the rationale for the effort, the challenges that it has posed, the development that has been done to make it work, and the benefits that it provides to the Netflix data platform teams. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Matthew Seal about the ways that Netflix is using Jupyter notebooks to bridge the gap between data roles Interview Introduction How did you get involved in the area of data management? Can you start by outlining the motivation for choosing Jupyter notebooks as the core interface for your data teams? Where are you using notebooks and where are you not? What is the technical infrastructure that you have built to suppport that design choice? Which team was driving the effort? Was it difficult to get buy in across teams? How much shared code have you been able to consolidate or reuse across teams/roles? Have you investigated the use of any of the other notebook platforms for similar workflows? What are some of the notebook anti-patterns that you have encountered and what conventions or tooling have you established to discourage them? What are some of the limitations of the notebook environment for the work that you are doing? What have been some of the most challenging aspects of building production workflows on top of Jupyter notebooks? What are some of the projects that are ongoing or planned for the future that you are most excited by? Contact Info Matthew Seal Email LinkedIn @codeseal on Twitter MSeal on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Netflix Notebook Blog Posts Nteract Tooling OpenGov Project Jupyter Zeppelin Notebooks Papermill Titus Commuter Scala Python R Emacs NBDime The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and this week I am sharing an episode from my other show, Podcast.__init__, about a project from Driven Data called Deon. It is a simple tool that generates a checklist of ethical considerations for the various stages of the lifecycle for data oriented projects. This is an important topic for all of the teams involved in the management and creation of projects that leverage data. So give it a listen and if you like what you hear, be sure to check out the other episodes at pythonpodcast.com Interview Introductions How did you get introduced to Python? Can you start by describing what Deon is and your motivation for creating it? Why a checklist, specifically? What’s the advantage of this over an oath, for example? What is unique to data science in terms of the ethical concerns, as compared to traditional software engineering? What is the typical workflow for a team that is using Deon in their projects? Deon ships with a default checklist but allows for customization. What are some common addendums that you have seen? Have you received pushback on any of the default items? How does Deon simplify communication around ethics across team boundaries? What are some of the most often overlooked items? What are some of the most difficult ethical concerns to comply with for a typical data science project? How has Deon helped you at Driven Data? What are the customer facing impacts of embedding a discussion of ethics in the product development process? Some of the items on the default checklist coincide with regulatory requirements. Are there any cases where regulation is in conflict with an ethical concern that you would like to see practiced? What are your hopes for the future of the Deon project? Keep In Touch Emily LinkedIn ejm714 on GitHub Peter LinkedIn @pjbull on Twitter pjbull on GitHub Driven Data @drivendataorg on Twitter drivendataorg on GitHub Website Picks Tobias Richard Bond Glass Art Emily Tandem Coffee in Portland, Maine Peter The Model Bakery in Saint Helena and Napa, California Links Deon Driven Data International Development Brookings Institution Stata Econometrics Metis Bootcamp Pandas Podcast Episode C# .NET Podcast.__init__ Episode On Software Ethics Jupyter Notebook Podcast Episode Word2Vec cookiecutter data science Logistic Regression The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary With the growth of the Hadoop ecosystem came a proliferation of implementations for the Hive table format. Unfortunately, with no formal specification, each project works slightly different which increases the difficulty of integration across systems. The Hive format is also built with the assumptions of a local filesystem which results in painful edge cases when leveraging cloud object storage for a data lake. In this episode Ryan Blue explains how his work on the Iceberg table format specification and reference implementation has allowed Netflix to improve the performance and simplify operations for their S3 data lake. This is a highly detailed and technical exploration of how a well-engineered metadata layer can improve the speed, accuracy, and utility of large scale, multi-tenant, cloud-native data platforms. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Ryan Blue about Iceberg, a Netflix project to implement a high performance table format for batch workloads Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Iceberg is and the motivation for creating it? Was the project built with open-source in mind or was it necessary to refactor it from an internal project for public use? How has the use of Iceberg simplified your work at Netflix? How is the reference implementation architected and how has it evolved since you first began work on it? What is involved in deploying it to a user’s environment? For someone who is interested in using Iceberg within their own environments, what is involved in integrating it with their existing query engine? Is there a migration path for pre-existing tables into the Iceberg format? How is schema evolution managed at the file level? How do you handle files on disk that don’t contain all of the fields specified in a table definition? One of the complicated problems in data modeling is managing table partitions. How does Iceberg help in that regard? What are the unique challenges posed by using S3 as the basis for a data lake? What are the benefits that outweigh the difficulties? What have been some of the most challenging or contentious details of the specification to define? What are some things that you have explicitly left out of the specification? What are your long-term goals for the Iceberg specification? Do you anticipate the reference implementation continuing to be used and maintained? Contact Info rdblue on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Iceberg Reference Implementation Iceberg Table Specification Netflix Hadoop Cloudera Avro Parquet Spark S3 HDFS Hive ORC S3mper Git Metacat Presto Pig DDL (Data Definition Language) Cost-Based Optimization The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

SummaryOne of the most complex aspects of managing data for analytical workloads is moving it from a transactional database into the data warehouse. What if you didn’t have to do that at all? MemSQL is a distributed database built to support concurrent use by transactional, application oriented, and analytical, high volume, workloads on the same hardware. In this episode the CEO of MemSQL describes how the company and database got started, how it is architected for scale and speed, and how it is being used in production. This was a deep dive on how to build a successful company around a powerful platform, and how that platform simplifies operations for enterprise grade data management.PreambleHello and welcome to the Data Engineering Podcast, the show about modern data managementWhen you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.And the team at Metis Machine has shipped a proof-of-concept integration between the Skafos machine learning platform and the Tableau business intelligence tool, meaning that your BI team can now run the machine learning models custom built by your data science team. If you think that sounds awesome (and it is) then join the free webinar with Metis Machine on October 11th at 2 PM ET (11 AM PT). Metis Machine will walk through the architecture of the extension, demonstrate its capabilities in real time, and illustrate the use case for empowering your BI team to modify and run machine learning models directly from Tableau. Go to metismachine.com/webinars now to register.Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chatYour host is Tobias Macey and today I’m interviewing Nikita Shamgunov about MemSQL, a newSQL database built for simultaneous transactional and analytic workloadsInterviewIntroductionHow did you get involved in the area of data management?Can you start by describing what MemSQL is and how the product and business first got started?What are the typical use cases for customers running MemSQL?What are the benefits of integrating the ingestion pipeline with the database engine? What are some typical ways that the ingest capability is leveraged by customers?How is MemSQL architected and how has the internal design evolved from when you first started working on it?Where does it fall on the axes of the CAP theorem?How much processing overhead is involved in the conversion from the column oriented data stored on disk to the row oriented data stored in memory?Can you describe the lifecycle of a write transaction?Can you discuss the techniques that are used in MemSQL to optimize for speed and overall system performance?How do you mitigate the impact of network latency throughout the cluster during query planning and execution?How much of the implementation of MemSQL is using custom built code vs. open source projects?What are some of the common difficulties that your customers encounter when building on top of or migrating to MemSQL?What have been some of the most challenging aspects of building and growing the technical and business implementation of MemSQL?When is MemSQL the wrong choice for a data platform?What do you have planned for the future of MemSQL?Contact Info@nikitashamgunov on TwitterLinkedInParting QuestionFrom your perspective, what is the biggest gap in the tooling or technology for data management today?LinksMemSQLNewSQLMicrosoft SQL ServerSt. Petersburg University of Fine Mechanics And OpticsCC++In-Memory DatabaseRAM (Random Access Memory)Flash StorageOracle DBPostgreSQLPodcast EpisodeKafkaKinesisWealth ManagementData WarehouseODBCS3HDFSAvroParquetData Serialization Podcast EpisodeBroadcast JoinShuffle JoinCAP TheoremApache ArrowLZ4S2 Geospatial LibrarySybaseSAP HanaKubernetesThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary There are countless sources of data that are publicly available for use. Unfortunately, combining those sources and making them useful in aggregate is a time consuming and challenging process. The team at Enigma builds a knowledge graph for use in your own data projects. In this episode Chris Groskopf explains the platform they have built to consume large varieties and volumes of public data for constructing a graph for serving to their customers. He discusses the challenges they are facing to scale the platform and engineering processes, as well as the workflow that they have established to enable testing of their ETL jobs. This is a great episode to listen to for ideas on how to organize a data engineering organization. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Chris Groskopf about Enigma and how the are using public data sources to build a knowledge graph Interview Introduction How did you get involved in the area of data management? Can you give a brief overview of what Enigma has built and what the motivation was for starting the company? How do you define the concept of a knowledge graph? What are the processes involved in constructing a knowledge graph? Can you describe the overall architecture of your data platform and the systems that you use for storing and serving your knowledge graph? What are the most challenging or unexpected aspects of building the knowledge graph that you have encountered? How do you manage the software lifecycle for your ETL code? What kinds of unit, integration, or acceptance tests do you run to ensure that you don’t introduce regressions in your processing logic? What are the current challenges that you are facing in building and scaling your data infrastructure? How does the fact that your data sources are primarily public influence your pipeline design and what challenges does it pose? What techniques are you using to manage accuracy and consistency in the data that you ingest? Can you walk through the lifecycle of the data that you process from acquisition through to delivery to your customers? What are the weak spots in your platform that you are planning to address in upcoming projects? If you were to start from scratch today, what would you have done differently? What are some of the most interesting or unexpected uses of your product that you have seen? What is in store for the future of Enigma? Contact Info Email Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Enigma Chicago Tribune NPR Quartz CSVKit Agate Knowledge Graph Taxonomy Concourse Airflow Docker S3 Data Lake Parquet Podcast Episode Spark AWS Neptune AWS Batch Money Laundering Jupyter Notebook Papermill Jupytext Cauldron: The Un-Notebook Podcast.__init__ Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary As your data needs scale across an organization the need for a carefully considered approach to collection, storage, organization, and access becomes increasingly critical. In this episode Todd Walter shares his considerable experience in data curation to clarify the many aspects that are necessary for a successful platform for your business. Using the metaphor of a museum curator carefully managing the precious resources on display and in the vaults, he discusses the various layers of an enterprise data strategy. This includes modeling the lifecycle of your information as a pipeline from the raw, messy, loosely structured records in your data lake, through a series of transformations and ultimately to your data warehouse. He also explains which layers are useful for the different members of the business, and which pitfalls to look out for along the path to a mature and flexible data platform. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Todd Walter about data curation and how to architect your data systems to support high quality, maintainable intelligence Interview Introduction How did you get involved in the area of data management? How do you define data curation? What are some of the high level concerns that are encapsulated in that effort? How does the size and maturity of a company affect the ways that they architect and interact with their data systems? Can you walk through the stages of an ideal lifecycle for data within the context of an organizations uses for it? What are some of the common mistakes that are made when designing a data architecture and how do they lead to failure? What has changed in terms of complexity and scope for data architecture and curation since you first started working in this space? As “big data” became more widely discussed the common mantra was to store everything because you never know when you’ll need the data that might get thrown away. As the industry is reaching a greater degree of maturity and more regulations are implemented there has been a shift to being more considerate as to what information gets stored and for how long. What are your views on that evolution and what is your litmus test for determining which data to keep? In terms of infrastructure, what are the components of a modern data architecture and how has that changed over the years? What is your opinion on the relative merits of a data warehouse vs a data lake and are they mutually exclusive? Once an architecture has been established, how do you allow for continued evolution to prevent stagnation and eventual failure? ETL has long been the default approach for building and enforcing data architecture, but there have been significant shifts in recent years due to the emergence of streaming systems and ELT approaches in new data warehouses. What are your thoughts on the landscape for managing data flows and migration and when to use which approach? What are some of the areas of data architecture and curation that are most often forgotten or ignored? What resources do you recommend for anyone who is interested in learning more about the landscape of data architecture and curation? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Teradata Data Architecture Data Curation Data Warehouse Chief Data Officer ETL (Extract, Transform, Load) Data Lake Metadata Data Lineage Data Provenance Strata Conference ELT (Extract, Load, Transform) Map-Reduce Hive Pig Spark Data Governance The intro and outro music is from The Hug by The Freak

Summary Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics Interview Introductions How did you get involved in the area of data engineering and data management? What is Snowplow Analytics and what problem were you trying to solve when you started the company? What is unique about customer event data from an ingestion and processing perspective? Challenges with properly matching up data between sources Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data? Cleanliness/accuracy What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly? Can you describe the overall architecture of the ingest pipeline that Snowplow provides? How has that architecture evolved from when you first started? What would you do differently if you were to start over today? Ensuring appropriate use of enrichment sources What have been some of the biggest challenges encountered while building and evolving Snowplow? What are some of the most interesting uses of your platform that you are aware of? Keep In Touch Alex @alexcrdean on Twitter LinkedIn Snowplow @snowplowdata on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Snowplow GitHub Deloitte Consulting OpenX Hadoop AWS EMR (Elastic Map-Reduce) Business Intelligence Data Warehousing Google Analytics CRM (Customer Relationship Management) S3 GDPR (General Data Protection Regulation) Kinesis Kafka Google Cloud Pub-Sub JSON-Schema Iglu IAB Bots And Spiders List Heap Analytics Podcast Interview Redshift SnowflakeDB Snowplow Insights Google Cloud Platform Azure GitLab The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Elasticsearch is a powerful tool for storing and analyzing data, but when using it for logs and other time oriented information it can become problematic to keep all of your history. Chaos Search was started to make it easy for you to keep all of your data and make it usable in S3, so that you can have the best of both worlds. In this episode the CTO, Thomas Hazel, and VP of Product, Pete Cheslock, describe how they have built a platform to let you keep all of your history, save money, and reduce your operational overhead. They also explain some of the types of data that you can use with Chaos Search, how to load it into S3, and when you might want to choose it over Amazon Athena for our serverless data analysis. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $/0 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Pete Cheslock and Thomas Hazel about Chaos Search and their effort to bring historical depth to your Elasticsearch data Interview Introduction How did you get involved in the area of data management? Can you start by explaining what you have built at Chaos Search and the problems that you are trying to solve with it? What types of data are you focused on supporting? What are the challenges inherent to scaling an elasticsearch infrastructure to large volumes of log or metric data? Is there any need for an Elasticsearch cluster in addition to Chaos Search? For someone who is using Chaos Search, what mechanisms/formats would they use for loading their data into S3? What are the benefits of implementing the Elasticsearch API on top of your data in S3 as opposed to using systems such as Presto or Drill to interact with the same information via SQL? Given that the S3 API has become a de facto standard for many other object storage platforms, what would be involved in running Chaos Search on data stored outside of AWS? What mechanisms do you use to allow for such drastic space savings of indexed data in S3 versus in an Elasticsearch cluster? What is the system architecture that you have built to allow for querying terabytes of data in S3? What are the biggest contributors to query latency and what have you done to mitigate them? What are the options for access control when running queries against the data stored in S3? What are some of the most interesting or unexpected uses of Chaos Search and access to large amounts of historical log information that you have seen? What are your plans for the future of Chaos Search? Contact Info Pete Cheslock @petecheslock on Twitter Website Thomas Hazel @thomashazel on Twitter LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Chaos Search AWS S3 Cassandra Elasticsearch Podcast Interview PostgreSQL Distributed Systems Information Theory Lucene Inverted Index Kibana Logstash NVMe AWS KMS Kinesis FluentD Parquet Athena Presto Drill Backblaze OpenStack Swift Minio EMR DataDog NewRelic Elastic Beats Metricbeat Graphite Snappy Scala Akka Elastalert Tensorflow X-Pack Data Lake The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary With the proliferation of data sources to give a more comprehensive view of the information critical to your business it is even more important to have a canonical view of the entities that you care about. Is customer number 342 in your ERP the same as Bob Smith on Twitter? Using master data management to build a data catalog helps you answer these questions reliably and simplify the process of building your business intelligence reports. In this episode the head of product at Tamr, Mark Marinelli, discusses the challenges of building a master data set, why you should have one, and some of the techniques that modern platforms and systems provide for maintaining it. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Mark Marinelli about data mastering for modern platforms Interview Introduction How did you get involved in the area of data management? Can you start by establishing a definition of data mastering that we can work from? How does the master data set get used within the overall analytical and processing systems of an organization? What is the traditional workflow for creating a master data set? What has changed in the current landscape of businesses and technology platforms that makes that approach impractical? What are the steps that an organization can take to evolve toward an agile approach to data mastering? At what scale of company or project does it makes sense to start building a master data set? What are the limitations of using ML/AI to merge data sets? What are the limitations of a golden master data set in practice? Are there particular formats of data or types of entities that pose a greater challenge when creating a canonical format for them? Are there specific problem domains that are more likely to benefit from a master data set? Once a golden master has been established, how are changes to that information handled in practice? (e.g. versioning of the data) What storage mechanisms are typically used for managing a master data set? Are there particular security, auditing, or access concerns that engineers should be considering when managing their golden master that goes beyond the rest of their data infrastructure? How do you manage latency issues when trying to reference the same entities from multiple disparate systems? What have you found to be the most common stumbling blocks for a group that is implementing a master data platform? What suggestions do you have to help prevent such a project from being derailed? What resources do you recommend for someone looking to learn more about the theoretical and practical aspects of data mastering for their organization? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Tamr Multi-Dimensional Database Master Data Management ETL EDW (Enterprise Data Warehouse) Waterfall Development Method Agile Development Method DataOps Feature Engineering Tableau Qlik Data Catalog PowerBI RDBMS (Relational Database Management System) The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary There are myriad reasons why data should be protected, and just as many ways to enforce it in tranist or at rest. Unfortunately, there is still a weak point where attackers can gain access to your unencrypted information. In this episode Ellison Anny Williams, CEO of Enveil, describes how her company uses homomorphic encryption to ensure that your analytical queries can be executed without ever having to decrypt your data. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Ellison Anne Williams about Enveil, a pioneering data security company protecting Data in Use Interview Introduction How did you get involved in the area of data security? Can you start by explaining what your mission is with Enveil and how the company got started? One of the core aspects of your platform is the principal of homomorphic encryption. Can you explain what that is and how you are using it? What are some of the challenges associated with scaling homomorphic encryption? What are some difficulties associated with working on encrypted data sets? Can you describe the underlying architecture for your data platform? How has that architecture evolved from when you first began building it? What are some use cases that are unlocked by having a fully encrypted data platform? For someone using the Enveil platform, what does their workflow look like? A major reason for never decrypting data is to protect it from attackers and unauthorized access. What are some of the remaining attack vectors? What are some aspects of the data being protected that still require additional consideration to prevent leaking information? (e.g. identifying individuals based on geographic data, or purchase patterns) What do you have planned for the future of Enveil? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data security today? Links Enveil NSA GDPR Intellectual Property Zero Trust Homomorphic Encryption Ciphertext Hadoop PII (Personally Identifiable Information) TLS (Transport Layer Security) Spark Elasticsearch Side-channel attacks Spectre and Meltdown The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary The way that you store your data can have a huge impact on the ways that it can be practically used. For a substantial number of use cases, the optimal format for storing and querying that information is as a graph, however databases architected around that use case have historically been difficult to use at scale or for serving fast, distributed queries. In this episode Manish Jain explains how DGraph is overcoming those limitations, how the project got started, and how you can start using it today. He also discusses the various cases where a graph storage layer is beneficial, and when you would be better off using something else. In addition he talks about the challenges of building a distributed, consistent database and the tradeoffs that were made to make DGraph a reality. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. If you have ever wished that you could use the same tools for versioning and distributing your data that you use for your software then you owe it to yourself to check out what the fine folks at Quilt Data have built. Quilt is an open source platform for building a sane workflow around your data that works for your whole team, including version history, metatdata management, and flexible hosting. Stop by their booth at JupyterCon in New York City on August 22nd through the 24th to say Hi and tell them that the Data Engineering Podcast sent you! After that, keep an eye on the AWS marketplace for a pre-packaged version of Quilt for Teams to deploy into your own environment and stop fighting with your data. Python has quickly become one of the most widely used languages by both data engineers and data scientists, letting everyone on your team understand each other more easily. However, it can be tough learning it when you’re just starting out. Luckily, there’s an easy way to get involved. Written by MIT lecturer Ana Bell and published by Manning Publications, Get Programming: Learn to code with Python is the perfect way to get started working with Python. Ana’s experience as a teacher of Python really shines through, as you get hands-on with the language without being drowned in confusing jargon or theory. Filled with practical examples and step-by-step lessons to take on, Get Programming is perfect for people who just want to get stuck in with Python. Get your copy of the book with a special 40% discount for Data Engineering Podcast listeners by going to dataengineeringpodcast.com/get-programming and use the discount code PodInit40! Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Manish Jain about DGraph, a low latency, high throughput, native and distributed graph database. Interview Introduction How did you get involved in the area of data management? What is DGraph and what motivated you to build it? Graph databases and graph algorithms have been part of the computing landscape for decades. What has changed in recent years to allow for the current proliferation of graph oriented storage systems? The graph space is becoming crowded in recent years. How does DGraph compare to the current set of offerings? What are some of the common uses of graph storage systems? What are some potential uses that are often overlooked? There are a few ways that graph structures and properties can be implemented, including the ability to store data in the vertices connecting nodes and the structures that can be contained within the nodes themselves. How is information represented in DGraph and what are the tradeoffs in the approach that you chose? How does the query interface and data storage in DGraph differ from other options? What are your opinions on the graph query languages that have been adopted by other storages systems, such as Gremlin, Cypher, and GSQL? How is DGraph architected and how has that architecture evolved from when it first started? How do you balance the speed and agility of schema on read with the additional application complexity that is required, as opposed to schema on write? In your documentation you contend that DGraph is a viable replacement for RDBMS-oriented primary storage systems. What are the switching costs for someone looking to make that transition? What are the limitations of DGraph in terms of scalability or usability? Where does it fall along the axes of the CAP theorem? For someone who

Summary The theory behind how a tool is supposed to work and the realities of putting it into practice are often at odds with each other. Learning the pitfalls and best practices from someone who has gained that knowledge the hard way can save you from wasted time and frustration. In this episode James Meickle discusses his recent experience building a new installation of Airflow. He points out the strengths, design flaws, and areas of improvement for the framework. He also describes the design patterns and workflows that his team has built to allow them to use Airflow as the basis of their data science platform. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing James Meickle about his experiences building a new Airflow installation Interview Introduction How did you get involved in the area of data management? What was your initial project requirement? What tooling did you consider in addition to Airflow? What aspects of the Airflow platform led you to choose it as your implementation target? Can you describe your current deployment architecture? How many engineers are involved in writing tasks for your Airflow installation? What resources were the most helpful while learning about Airflow design patterns? How have you architected your DAGs for deployment and extensibility? What kinds of tests and automation have you put in place to support the ongoing stability of your deployment? What are some of the dead-ends or other pitfalls that you encountered during the course of this project? What aspects of Airflow have you found to be lacking that you would like to see improved? What did you wish someone had told you before you started work on your Airflow installation? If you were to start over would you make the same choice? If Airflow wasn’t available what would be your second choice? What are your next steps for improvements and fixes? Contact Info @eronarn on Twitter Website eronarn on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Quantopian Harvard Brain Science Initiative DevOps Days Boston Google Maps API Cron ETL (Extract, Transform, Load) Azkaban Luigi AWS Glue Airflow Pachyderm Podcast Interview AirBnB Python YAML Ansible REST (Representational State Transfer) SAML (Security Assertion Markup Language) RBAC (Role-Based Access Control) Maxime Beauchemin Medium Blog Celery Dask Podcast Interview PostgreSQL Podcast Interview Redis Cloudformation Jupyter Notebook Qubole Astronomer Podcast Interview Gunicorn Kubernetes Airflow Improvement Proposals Python Enhancement Proposals (PEP) The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary One of the longest running and most popular open source database projects is PostgreSQL. Because of its extensibility and a community focus on stability it has stayed relevant as the ecosystem of development environments and data requirements have changed and evolved over its lifetime. It is difficult to capture any single facet of this database in a single conversation, let alone the entire surface area, but in this episode Jonathan Katz does an admirable job of it. He explains how Postgres started and how it has grown over the years, highlights the fundamental features that make it such a popular choice for application developers, and the ongoing efforts to add the complex features needed by the demanding workloads of today’s data layer. To cap it off he reviews some of the exciting features that the community is working on building into future releases. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Jonathan Katz about a high level view of PostgreSQL and the unique capabilities that it offers Interview Introduction How did you get involved in the area of data management? How did you get involved in the Postgres project? For anyone who hasn’t used it, can you describe what PostgreSQL is? Where did Postgres get started and how has it evolved over the intervening years? What are some of the primary characteristics of Postgres that would lead someone to choose it for a given project? What are some cases where Postgres is the wrong choice? What are some of the common points of confusion for new users of PostGreSQL? (particularly if they have prior database experience) The recent releases of Postgres have had some fairly substantial improvements and new features. How does the community manage to balance stability and reliability against the need to add new capabilities? What are the aspects of Postgres that allow it to remain relevant in the current landscape of rapid evolution at the data layer? Are there any plans to incorporate a distributed transaction layer into the core of the project along the lines of what has been done with Citus or CockroachDB? What is in store for the future of Postgres? Contact Info @jkatz05 on Twitter jkatz on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links PostgreSQL Crunchy Data Venuebook Paperless Post LAMP Stack MySQL PHP SQL ORDBMS Edgar Codd A Relational Model of Data for Large Shared Data Banks Relational Algebra Oracle DB UC Berkeley Dr. Michael Stonebraker Ingres Informix QUEL ANSI C CVS BSD License UUID JSON XML HStore PostGIS BTree Index GIN Index GIST Index KNN GIST SPGIST Full Text Search BRIN Index WAL (Write-Ahead Log) SQLite PGAdmin Vim Emacs Linux OLAP (Online Analytical Processing) Postgres IRC Postgres Slack Postgres Conferences UPSERT Postgres Roadmap CockroachDB Podcast Interview Citus Data Podcast Interview The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary With the attention being paid to the systems that power large volumes of high velocity data it is easy to forget about the value of data collection at human scales. Ona is a company that is building technologies to support mobile data collection, analysis of the aggregated information, and user-friendly presentations. In this episode CTO Peter Lubell-Doughtie describes the architecture of the platform, the types of environments and use cases where it is being employed, and the value of small data. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Peter Lubell-Doughtie about using Ona for collecting data and processing it with Canopy Interview Introduction How did you get involved in the area of data management? What is Ona and how did the company get started? What are some examples of the types of customers that you work with? What types of data do you support in your collection platform? What are some of the mechanisms that you use to ensure the accuracy of the data that is being collected by users? Does your mobile collection platform allow for anyone to submit data without having to be associated with a given account or organization? What are some of the integration challenges that are unique to the types of data that get collected by mobile field workers? Can you describe the flow of the data from collection through to analysis? To help improve the utility of the data being collected you have started building Canopy. What was the tipping point where it became worth the time and effort to start that project? What are the architectural considerations that you factored in when designing it? What have you found to be the most challenging or unexpected aspects of building an enterprise data warehouse for general users? What are your plans for the future of Ona and Canopy? Contact Info Email pld on Github Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links OpenSRP Ona Canopy Open Data Kit Earth Institute at Columbia University Sustainable Engineering Lab WHO Bill and Melinda Gates Foundation XLSForms PostGIS Kafka Druid Superset Postgres Ansible Docker Terraform The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary When working with large volumes of data that you need to access in parallel across multiple instances you need a distributed filesystem that will scale with your workload. Even better is when that same system provides multiple paradigms for interacting with the underlying storage. Ceph is a highly available, highly scalable, and performant system that has support for object storage, block storage, and native filesystem access. In this episode Sage Weil, the creator and lead maintainer of the project, discusses how it got started, how it works, and how you can start using it on your infrastructure today. He also explains where it fits in the current landscape of distributed storage and the plans for future improvements. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Sage Weil about Ceph, an open source distributed file system that supports block storage, object storage, and a file system interface. Interview Introduction How did you get involved in the area of data management? Can you start with an overview of what Ceph is? What was the motivation for starting the project? What are some of the most common use cases for Ceph? There are a large variety of distributed file systems. How would you characterize Ceph as it compares to other options (e.g. HDFS, GlusterFS, LionFS, SeaweedFS, etc.)? Given that there is no single point of failure, what mechanisms do you use to mitigate the impact of network partitions? What mechanisms are available to ensure data integrity across the cluster? How is Ceph implemented and how has the design evolved over time? What is required to deploy and manage a Ceph cluster? What are the scaling factors for a cluster? What are the limitations? How does Ceph handle mixed write workloads with either a high volume of small files or a smaller volume of larger files? In services such as S3 the data is segregated from block storage options like EBS or EFS. Since Ceph provides all of those interfaces in one project is it possible to use each of those interfaces to the same data objects in a Ceph cluster? In what situations would you advise someone against using Ceph? What are some of the most interested, unexpected, or challenging aspects of working with Ceph and the community? What are some of the plans that you have for the future of Ceph? Contact Info Email @liewegas on Twitter liewegas on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Ceph Red Hat DreamHost UC Santa Cruz Los Alamos National Labs Dream Objects OpenStack Proxmox POSIX GlusterFS Hadoop Ceph Architecture Paxos relatime Prometheus Zabbix Kubernetes NVMe DNS-SD Consul EtcD DNS SRV Record Zeroconf Bluestore XFS Erasure Coding NFS Seastar Rook The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Data integration and routing is a constantly evolving problem and one that is fraught with edge cases and complicated requirements. The Apache NiFi project models this problem as a collection of data flows that are created through a self-service graphical interface. This framework provides a flexible platform for building a wide variety of integrations that can be managed and scaled easily to fit your particular needs. In this episode project members Kevin Doran and Andy LoPresto discuss the ways that NiFi can be used, how to start using it in your environment, and plans for future development. They also explained how it fits in the broad landscape of data tools, the interesting and challenging aspects of the project, and how to build new extensions. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Doran and Andy LoPresto about Apache NiFi Interview Introduction How did you get involved in the area of data management? Can you start by explaining what NiFi is? What is the motivation for building a GUI as the primary interface for the tool when the current trend is to represent everything as code? How did you get involved with the project? Where does it sit in the broader landscape of data tools? Does the data that is processed by NiFi flow through the servers that it is running on (á la Spark/Flink/Kafka), or does it orchestrate actions on other systems (á la Airflow/Oozie)? How do you manage versioning and backup of data flows, as well as promoting them between environments? One of the advertised features is tracking provenance for data flows that are managed by NiFi. How is that data collected and managed? What types of reporting are available across this information? What are some of the use cases or requirements that lend themselves well to being solved by NiFi? When is NiFi the wrong choice? What is involved in deploying and scaling a NiFi installation? What are some of the system/network parameters that should be considered? What are the scaling limitations? What have you found to be some of the most interesting, unexpected, and/or challenging aspects of building and maintaining the NiFi project and community? What do you have planned for the future of NiFi? Contact Info Kevin Doran @kevdoran on Twitter Email Andy LoPresto @yolopey on Twitter Email Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links NiFi HortonWorks DataFlow HortonWorks Apache Software Foundation Apple CSV XML JSON Perl Python Internet Scale Asset Management Documentum DataFlow NSA (National Security Agency) 24 (TV Show) Technology Transfer Program Agile Software Development Waterfall Spark Flink Kafka Oozie Luigi Airflow FluentD ETL (Extract, Transform, and Load) ESB (Enterprise Service Bus) MiNiFi Java C++ Provenance Kubernetes Apache Atlas Data Governance Kibana K-Nearest Neighbors DevOps DSL (Domain Specific Language) NiFi Registry Artifact Repository Nexus NiFi CLI Maven Archetype IoT Docker Backpressure NiFi Wiki TLS (Transport Layer Security) Mozilla TLS Observatory NiFi Flow Design System Data Lineage GDPR (General Data Protection Regulation) The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Data is often messy or incomplete, requiring human intervention to make sense of it before being usable as input to machine learning projects. This is problematic when the volume scales beyond a handful of records. In this episode Dr. Cheryl Martin, Chief Data Scientist for Alegion, discusses the importance of properly labeled information for machine learning and artificial intelligence projects, the systems that they have built to scale the process of incorporating human intelligence in the data preparation process, and the challenges inherent to such an endeavor. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Cheryl Martin, chief data scientist at Alegion, about data labelling at scale Interview Introduction How did you get involved in the area of data management? To start, can you explain the problem space that Alegion is targeting and how you operate? When is it necessary to include human intelligence as part of the data lifecycle for ML/AI projects? What are some of the biggest challenges associated with managing human input to data sets intended for machine usage? For someone who is acting as human-intelligence provider as part of the workforce, what does their workflow look like? What tools and processes do you have in place to ensure the accuracy of their inputs? How do you prevent bad actors from contributing data that would compromise the trained model? What are the limitations of crowd-sourced data labels? When is it beneficial to incorporate domain experts in the process? When doing data collection from various sources, how do you ensure that intellectual property rights are respected? How do you determine the taxonomies to be used for structuring data sets that are collected, labeled or enriched for your customers? What kinds of metadata do you track and how is that recorded/transmitted? Do you think that human intelligence will be a necessary piece of ML/AI forever? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Alegion University of Texas at Austin Cognitive Science Labeled Data Mechanical Turk Computer Vision Sentiment Analysis Speech Recognition Taxonomy Feature Engineering The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Collaboration, distribution, and installation of software projects is largely a solved problem, but the same cannot be said of data. Every data team has a bespoke means of sharing data sets, versioning them, tracking related metadata and changes, and publishing them for use in the software systems that rely on them. The CEO and founder of Quilt Data, Kevin Moore, was sufficiently frustrated by this problem to create a platform that attempts to be the means by which data can be as collaborative and easy to work with as GitHub and your favorite programming language. In this episode he explains how the project came to be, how it works, and the many ways that you can start using it today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Moore about Quilt Data, a platform and tooling for packaging, distributing, and versioning data Interview Introduction How did you get involved in the area of data management? What is the intended use case for Quilt and how did the project get started? Can you step through a typical workflow of someone using Quilt? How does that change as you go from a single user to a team of data engineers and data scientists? Can you describe the elements of what a data package consists of? What was your criteria for the file formats that you chose? How is Quilt architected and what have been the most significant changes or evolutions since you first started? How is the data registry implemented? What are the limitations or edge cases that you have run into? What optimizations have you made to accelerate synchronization of the data to and from the repository? What are the limitations in terms of data volume, format, or usage? What is your goal with the business that you have built around the project? What are your plans for the future of Quilt? Contact Info Email LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Quilt Data GitHub Jobs Reproducible Data Dependencies in Jupyter Reproducible Machine Learning with Jupyter and Quilt Allen Institute: Programmatic Data Access with Quilt Quilt Example: MissingNo Oracle Pandas Jupyter Ycombinator Data.World Podcast Episode with CTO Bryon Jacob Kaggle Parquet HDF5 Arrow PySpark Excel Scala Binder Merkle Tree Allen Institute for Cell Science Flask PostGreSQL Docker Airflow Quilt Teams Hive Hive Metastore PrestoDB Podcast Episode Netflix Iceberg Kubernetes Helm The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Web and mobile analytics are an important part of any business, and difficult to get right. The most frustrating part is when you realize that you haven’t been tracking a key interaction, having to write custom logic to add that event, and then waiting to collect data. Heap is a platform that automatically tracks every event so that you can retroactively decide which actions are important to your business and easily build reports with or without SQL. In this episode Dan Robinson, CTO of Heap, describes how they have architected their data infrastructure, how they build their tracking agents, and the data virtualization layer that enables users to define their own labels. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Dan Robinson about Heap and their approach to collecting, storing, and analyzing large volumes of data Interview Introduction How did you get involved in the area of data management? Can you start by giving a brief overview of Heap? One of your differentiating features is the fact that you capture every interaction on web and mobile platforms for your customers. How do you prevent the user experience from suffering as a result of network congestion, while ensuring the reliable delivery of that data? Can you walk through the lifecycle of a single event from source to destination and the infrastructure components that it traverses to get there? Data collected in a user’s browser can often be messy due to various browser plugins, variations in runtime capabilities, etc. How do you ensure the integrity and accuracy of that information? What are some of the difficulties that you have faced in establishing a representation of events that allows for uniform processing and storage? What is your approach for merging and enriching event data with the information that you retrieve from your supported integrations? What challenges does that pose in your processing architecture? What are some of the problems that you have had to deal with to allow for processing and storing such large volumes of data? How has that architecture changed or evolved over the life of the company? What are some changes that you are anticipating in the near future? Can you describe your approach for synchronizing customer data with their individual Redshift instances and the difficulties that entails? What are some of the most interesting challenges that you have faced while building the technical and business aspects of Heap? What changes have been necessary as a result of GDPR? What are your plans for the future of Heap? Contact Info @danlovesproofs on twitter [email protected] @drob on github heapanalytics.com / @heap on twitter https://heapanalytics.com/blog/category/engineering?utm_source=rss&utm_medium=rss Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Heap Palantir User Analytics Google Analytics Piwik Mixpanel Hubspot Jepsen Chaos Engineering Node.js Kafka Scala Citus React MobX Redshift Heap SQL BigQuery Webhooks Drip Data Virtualization DNS PII SOC2 The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Peter Mattis about CockroachDB, the SQL database for global cloud services Interview Introduction How did you get involved in the area of data management? What was the motivation for creating CockroachDB and building a business around it? Can you describe the architecture of CockroachDB and how it supports distributed ACID transactions? What are some of the tradeoffs that are necessary to allow for georeplicated data with distributed transactions? What are some of the problems that you have had to work around in the RAFT protocol to provide reliable operation of the clustering mechanism? Go is an unconventional language for building a database. What are the pros and cons of that choice? What are some of the common points of confusion that users of CockroachDB have when operating or interacting with it? What are the edge cases and failure modes that users should be aware of? I know that your SQL syntax is PostGreSQL compatible, so is it possible to use existing ORMs unmodified with CockroachDB? What are some examples of extensions that are specific to CockroachDB? What are some of the most interesting uses of CockroachDB that you have seen? When is CockroachDB the wrong choice? What do you have planned for the future of CockroachDB? Contact Info Peter LinkedIn petermattis on GitHub @petermattis on Twitter Cockroach Labs @CockroackDB on Twitter Website cockroachdb on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links CockroachDB Cockroach Labs SQL Google Bigtable Spanner NoSQL RDBMS (Relational Database Management System) “Big Iron” (colloquial term for mainframe computers) RAFT Consensus Algorithm Consensus MVCC (Multiversion Concurrency Control) Isolation Etcd GDPR Golang C++ Garbage Collection Metaprogramming Rust Static Linking Docker Kubernetes CAP Theorem PostGreSQL ORM (Object Relational Mapping) Information Schema PG Catalog Interleaved Tables Vertica Spark Change Data Capture The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Using a multi-model database in your applications can greatly reduce the amount of infrastructure and complexity required. ArangoDB is a storage engine that supports documents, dey/value, and graph data formats, as well as being fast and scalable. In this episode Jan Steeman and Jan Stücke explain where Arango fits in the crowded database market, how it works under the hood, and how you can start working with it today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Jan Stücke and Jan Steeman about ArangoDB, a multi-model distributed database for graph, document, and key/value storage. Interview Introduction How did you get involved in the area of data management? Can you give a high level description of what ArangoDB is and the motivation for creating it? What is the story behind the name? How is ArangoDB constructed? How does the underlying engine store the data to allow for the different ways of viewing it? What are some of the benefits of multi-model data storage? When does it become problematic? For users who are accustomed to a relational engine, how do they need to adjust their approach to data modeling when working with Arango? How does it compare to OrientDB? What are the options for scaling a running system? What are the limitations in terms of network architecture or data volumes? One of the unique aspects of ArangoDB is the Foxx framework for embedding microservices in the data layer. What benefits does that provide over a three tier architecture? What mechanisms do you have in place to prevent data breaches from security vulnerabilities in the Foxx code? What are some of the most interesting or surprising uses of this functionality that you have seen? What are some of the most challenging technical and business aspects of building and promoting ArangoDB? What do you have planned for the future of ArangoDB? Contact Info Jan Steemann jsteemann on GitHub @steemann on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links ArangoDB Köln Multi-model Database Graph Algorithms Apache 2 C++ ArangoDB Foxx Raft Protocol Target Partners RocksDB AQL (ArangoDB Query Language) OrientDB PostGreSQL OrientDB Studio Google Spanner 3-Tier Architecture Thomson-Reuters Arango Search Dell EMC Google S2 Index ArangoDB Geographic Functionality JSON Schema The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Building an ETL pipeline is a common need across businesses and industries. It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary. Rather than duplicating the efforts of other engineers it might be best to use a hosted service to handle the plumbing so that you can focus on the parts that actually matter for your business. In this episode CTO and co-founder of Alooma, Yair Weinberger, explains how the platform addresses the common needs of data collection, manipulation, and storage while allowing for flexible processing. He describes the motivation for starting the company, how their infrastructure is architected, and the challenges of supporting multi-tenancy and a wide variety of integrations. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Yair Weinberger about Alooma, a company providing data pipelines as a service Interview Introduction How did you get involved in the area of data management? What is Alooma and what is the origin story? How is the Alooma platform architected? I want to go into stream VS batch here What are the most challenging components to scale? How do you manage the underlying infrastructure to support your SLA of 5 nines? What are some of the complexities introduced by processing data from multiple customers with various compliance requirements? How do you sandbox user’s processing code to avoid security exploits? What are some of the potential pitfalls for automatic schema management in the target database? Given the large number of integrations, how do you maintain the What are some challenges when creating integrations, isn’t it simply conforming with an external API? For someone getting started with Alooma what does the workflow look like? What are some of the most challenging aspects of building and maintaining Alooma? What are your plans for the future of Alooma? Contact Info LinkedIn @yairwein on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Alooma Convert Media Data Integration ESB (Enterprise Service Bus) Tibco Mulesoft ETL (Extract, Transform, Load) Informatica Microsoft SSIS OLAP Cube S3 Azure Cloud Storage Snowflake DB Redshift BigQuery Salesforce Hubspot Zendesk Spark The Log: What every software engineer should know about real-time data’s unifying abstraction by Jay Kreps RDBMS (Relational Database Management System) SaaS (Software as a Service) Change Data Capture Kafka Storm Google Cloud PubSub Amazon Kinesis Alooma Code Engine Zookeeper Idempotence Kafka Streams Kubernetes SOC2 Jython Docker Python Javascript Ruby Scala PII (Personally Identifiable Information) GDPR (General Data Protection Regulation) Amazon EMR (Elastic Map Reduce) Sequoia Capital Lightspeed Investors Redis Aerospike Cassandra MongoDB The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Most businesses end up with data in a myriad of places with varying levels of structure. This makes it difficult to gain insights from across departments, projects, or people. Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project. In this episode he describes how Presto is architected, how you can use it for your analytics, and the work that he is doing at Starburst Data. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kamil Bajda-Pawlikowski about Presto and his experiences with supporting it at Starburst Data Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Presto is? What are some of the common use cases and deployment patterns for Presto? How does Presto compare to Drill or Impala? What is it about Presto that led you to building a business around it? What are some of the most challenging aspects of running and scaling Presto? For someone who is using the Presto SQL interface, what are some of the considerations that they should keep in mind to avoid writing poorly performing queries? How does Presto represent data for translating between its SQL dialect and the API of the data stores that it interfaces with? What are some cases in which Presto is not the right solution? What types of support have you found to be the most commonly requested? What are some of the types of tooling or improvements that you have made to Presto in your distribution? What are some of the notable changes that your team has contributed upstream to Presto? Contact Info Website E-mail Twitter – @starburstdata Twitter – @prestodb Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Starburst Data Presto Hadapt Hadoop Hive Teradata PrestoCare Cost Based Optimizer ANSI SQL Spill To Disk Tempto Benchto Geospatial Functions Cassandra Accumulo Kafka Redis PostGreSQL The intro and outro music is from The Hug by The Freak Fandango Orchestra / {CC BY-SA](http://creativecommons.org/licenses/by-sa/3.0/)?utm_source=rss&utm_medium=rssSupport Data Engineering Podcast

Summary The Open Data Science Conference brings together a variety of data professionals each year in Boston. This week’s episode consists of a pair of brief interviews conducted on-site at the conference. First up you’ll hear from Andy Eschbacher of Carto. He dscribes some of the complexities inherent to working with geospatial data, how they are handling it, and some of the interesting use cases that they enable for their customers. Next is Todd Blaschka, COO of TigerGraph. He explains how graph databases differ from relational engines, where graph algorithms are useful, and how TigerGraph is built to alow for fast and scalable operation. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and last week I attended the Open Data Science Conference in Boston and recorded a few brief interviews on-site. In this second part you will hear from Andy Eschbacher of Carto about the challenges of managing geospatial data, as well as Todd Blaschka of TigerGraph about graph databases and how his company has managed to build a fast and scalable platform for graph storage and traversal. Interview Andy Eschbacher From Carto What are the challenges associated with storing geospatial data? What are some of the common misconceptions that people have about working with geospatial data? Contact Info andy-esch on GitHub @MrEPhysics on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Carto Geospatial Analysis GeoJSON Todd Blaschka From TigerGraph What are graph databases and how do they differ from relational engines? What are some of the common difficulties that people have when deling with graph algorithms? How does data modeling for graph databases differ from relational stores? Contact Info LinkedIn @toddblaschka on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links TigerGraph Graph Databases The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary The Open Data Science Conference brings together a variety of data professionals each year in Boston. This week’s episode consists of a pair of brief interviews conducted on-site at the conference. First up you’ll hear from Alan Anders, the CTO of Applecart about their challenges with getting Spark to scale for constructing an entity graph from multiple data sources. Next I spoke with Stepan Pushkarev, the CEO, CTO, and Co-Founder of Hydrosphere.io about the challenges of running machine learning models in production and how his team tracks key metrics and samples production data to re-train and re-deploy those models for better accuracy and more robust operation. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and this week I attended the Open Data Science Conference in Boston and recorded a few brief interviews on-site. First up you’ll hear from Alan Anders, the CTO of Applecart about their challenges with getting Spark to scale for constructing an entity graph from multiple data sources. Next I spoke with Stepan Pushkarev, the CEO, CTO, and Co-Founder of Hydrosphere.io about the challenges of running machine learning models in production and how his team tracks key metrics and samples production data to re-train and re-deploy those models for better accuracy and more robust operation. Interview Alan Anders from Applecart What are the challenges of gathering and processing data from multiple data sources and representing them in a unified manner for merging into single entities? What are the biggest technical hurdles at Applecart? Contact Info @alanjanders on Twitter LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Spark DataBricks DataBricks Delta Applecart Stepan Pushkarev from Hydrosphere.io What is Hydropshere.io? What metrics do you track to determine when a machine learning model is not producing an appropriate output? How do you determine which data points to sample for retraining the model? How does the role of a machine learning engineer differ from data engineers and data scientists? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Hydrosphere Machine Learning Engineer The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Business Intelligence software is often cumbersome and requires specialized knowledge of the tools and data to be able to ask and answer questions about the state of the organization. Metabase is a tool built with the goal of making the act of discovering information and asking questions of an organizations data easy and self-service for non-technical users. In this episode the CEO of Metabase, Sameer Al-Sakran, discusses how and why the project got started, the ways that it can be used to build and share useful reports, some of the useful features planned for future releases, and how to get it set up to start using it in your environment. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Sameer Al-Sakran about Metabase, a free and open source tool for self service business intelligence Interview Introduction How did you get involved in the area of data management? The current goal for most companies is to be “data driven”. How would you define that concept? How does Metabase assist in that endeavor? What is the ratio of users that take advantage of the GUI query builder as opposed to writing raw SQL? What level of complexity is possible with the query builder? What have you found to be the typical use cases for Metabase in the context of an organization? How do you manage scaling for large or complex queries? What was the motivation for using Clojure as the language for implementing Metabase? What is involved in adding support for a new data source? What are the differentiating features of Metabase that would lead someone to choose it for their organization? What have been the most challenging aspects of building and growing Metabase, both from a technical and business perspective? What do you have planned for the future of Metabase? Contact Info Sameer salsakran on GitHub @sameer_alsakran on Twitter LinkedIn Metabase Website @metabase on Twitter metabase on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Expa Metabase Blackjet Hadoop Imeem Maslow’s Hierarchy of Data Needs 2 Sided Marketplace Honeycomb Interview Excel Tableau Go-JEK Clojure React Python Scala JVM Redash How To Lie With Data Stripe Braintree Payments The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary The information about how data is acquired and processed is often as important as the data itself. For this reason metadata management systems are built to track the journey of your business data to aid in analysis, presentation, and compliance. These systems are frequently cumbersome and difficult to maintain, so Octopai was founded to alleviate that burden. In this episode Amnon Drori, CEO and co-founder of Octopai, discusses the business problems he witnessed that led him to starting the company, how their systems are able to provide valuable tools and insights, and the direction that their product will be taking in the future. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Amnon Drori about OctopAI and the benefits of metadata management Interview Introduction How did you get involved in the area of data management? What is OctopAI and what was your motivation for founding it? What are some of the types of information that you classify and collect as metadata? Can you talk through the architecture of your platform? What are some of the challenges that are typically faced by metadata management systems? What is involved in deploying your metadata collection agents? Once the metadata has been collected what are some of the ways in which it can be used? What mechanisms do you use to ensure that customer data is segregated? How do you identify and handle sensitive information during the collection step? What are some of the most challenging aspects of your technical and business platforms that you have faced? What are some of the plans that you have for OctopAI going forward? Contact Info Amnon LinkedIn @octopai_amnon on Twitter OctopAI @OctopaiBI on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links OctopAI Metadata Metadata Management Data Integrity CRM (Customer Relationship Management) ERP (Enterprise Resource Planning) Business Intelligence ETL (Extract, Transform, Load) Informatica SAP Data Governance SSIS (SQL Server Integration Services) Vertica Airflow Luigi Oozie GDPR (General Data Privacy Regulation) Root Cause Analysis The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary The rate of change in the data engineering industry is alternately exciting and exhausting. Joe Crobak found his way into the work of data management by accident as so many of us do. After being engrossed with researching the details of distributed systems and big data management for his work he began sharing his findings with friends. This led to his creation of the Hadoop Weekly newsletter, which he recently rebranded as the Data Engineering Weekly newsletter. In this episode he discusses his experiences working as a data engineer in industry and at the USDS, his motivations and methods for creating a newsleteter, and the insights that he has gleaned from it. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Joe Crobak about his work maintaining the Data Engineering Weekly newsletter, and the challenges of keeping up with the data engineering industry. Interview Introduction How did you get involved in the area of data management? What are some of the projects that you have been involved in that were most personally fulfilling? As an engineer at the USDS working on the healthcare.gov and medicare systems, what were some of the approaches that you used to manage sensitive data? Healthcare.gov has a storied history, how did the systems for processing and managing the data get architected to handle the amount of load that it was subjected to? What was your motivation for starting a newsletter about the Hadoop space? Can you speak to your reasoning for the recent rebranding of the newsletter? How much of the content that you surface in your newsletter is found during your day-to-day work, versus explicitly searching for it? After over 5 years of following the trends in data analytics and data infrastructure what are some of the most interesting or surprising developments? What have you found to be the fundamental skills or areas of experience that have maintained relevance as new technologies in data engineering have emerged? What is your workflow for finding and curating the content that goes into your newsletter? What is your personal algorithm for filtering which articles, tools, or commentary gets added to the final newsletter? How has your experience managing the newsletter influenced your areas of focus in your work and vice-versa? What are your plans going forward? Contact Info Data Eng Weekly Email Twitter – @joecrobak Twitter – @dataengweekly Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links USDS National Labs Cray Amazon EMR (Elastic Map-Reduce) Recommendation Engine Netflix Prize Hadoop Cloudera Puppet healthcare.gov Medicare Quality Payment Program HIPAA NIST National Institute of Standards and Technology PII (Personally Identifiable Information) Threat Modeling Apache JBoss Apache Web Server MarkLogic JMS (Java Message Service) Load Balancer COBOL Hadoop Weekly Data Engineering Weekly Foursquare NiFi Kubernetes Spark Flink Stream Processing DataStax RSS The Flavors of Data Science and Engineering CQRS Change Data Capture Jay Kreps The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Managing an analytics project can be difficult due to the number of systems involved and the need to ensure that new information can be delivered quickly and reliably. That challenge can be met by adopting practices and principles from lean manufacturing and agile software development, and the cross-functional collaboration, feedback loops, and focus on automation in the DevOps movement. In this episode Christopher Bergh discusses ways that you can start adding reliability and speed to your workflow to deliver results with confidence and consistency. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Christopher Bergh about DataKitchen and the rise of DataOps Interview Introduction How did you get involved in the area of data management? How do you define DataOps? How does it compare to the practices encouraged by the DevOps movement? How does it relate to or influence the role of a data engineer? How does a DataOps oriented workflow differ from other existing approaches for building data platforms? One of the aspects of DataOps that you call out is the practice of providing multiple environments to provide a platform for testing the various aspects of the analytics workflow in a non-production context. What are some of the techniques that are available for managing data in appropriate volumes across those deployments? The practice of testing logic as code is fairly well understood and has a large set of existing tools. What have you found to be some of the most effective methods for testing data as it flows through a system? One of the practices of DevOps is to create feedback loops that can be used to ensure that business needs are being met. What are the metrics that you track in your platform to define the value that is being created and how the various steps in the workflow are proceeding toward that goal? In order to keep feedback loops fast it is necessary for tests to run quickly. How do you balance the need for larger quantities of data to be used for verifying scalability/performance against optimizing for cost and speed in non-production environments? How does the DataKitchen platform simplify the process of operationalizing a data analytics workflow? As the need for rapid iteration and deployment of systems to capture, store, process, and analyze data becomes more prevalent how do you foresee that feeding back into the ways that the landscape of data tools are designed and developed? Contact Info LinkedIn @ChrisBergh on Twitter Email Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links DataOps Manifesto DataKitchen 2017: The Year Of DataOps Air Traffic Control Chief Data Officer (CDO) Gartner W. Edwards Deming DevOps Total Quality Management (TQM) Informatica Talend Agile Development Cattle Not Pets IDE (Integrated Development Environment) Tableau Delphix Dremio Pachyderm Continuous Delivery by Jez Humble and Dave Farley SLAs (Service Level Agreements) XKCD Image Recognition Comic Airflow Luigi DataKitchen Documentation Continuous Integration Continous Delivery Docker Version Control Git Looker The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Cloud computing and ubiquitous virtualization have changed the ways that our applications are built and deployed. This new environment requires a new way of tracking and addressing the security of our systems. ThreatStack is a platform that collects all of the data that your servers generate and monitors for unexpected anomalies in behavior that would indicate a breach and notifies you in near-realtime. In this episode ThreatStack’s director of operations, Pete Cheslock, and senior infrastructure security engineer, Patrick Cable, discuss the data infrastructure that supports their platform, how they capture and process the data from client systems, and how that information can be used to keep your systems safe from attackers. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Pete Cheslock and Pat Cable about the data infrastructure and security controls at ThreatStack Interview Introduction How did you get involved in the area of data management? Why don’t you start by explaining what ThreatStack does? What was lacking in the existing options (services and self-hosted/open source) that ThreatStack solves for? Can you describe the type(s) of data that you collect and how it is structured? What is the high level data infrastructure that you use for ingesting, storing, and analyzing your customer data? How do you ensure a consistent format of the information that you receive? How do you ensure that the various pieces of your platform are deployed using the proper configurations and operating as intended? How much configuration do you provide to the end user in terms of the captured data, such as sampling rate or additional context? I understand that your original architecture used RabbitMQ as your ingest mechanism, which you then migrated to Kafka. What was your initial motivation for that change? How much of a benefit has that been in terms of overall complexity and cost (both time and infrastructure)? How do you ensure the security and provenance of the data that you collect as it traverses your infrastructure? What are some of the most common vulnerabilities that you detect in your client’s infrastructure? For someone who wants to start using ThreatStack, what does the setup process look like? What have you found to be the most challenging aspects of building and managing the data processes in your environment? What are some of the projects that you have planned to improve the capacity or capabilities of your infrastructure? Contact Info Pete Cheslock @petecheslock on Twitter Website petecheslock on GitHub Patrick Cable @patcable on Twitter Website patcable on GitHub ThreatStack Website @threatstack on Twitter threatstack on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links ThreatStack SecDevOps Sonian EC2 Snort Snorby Suricata Tripwire Syscall (System Call) AuditD CloudTrail Naxsi Cloud Native File Integrity Monitoring (FIM) Amazon Web Services (AWS) RabbitMQ ZeroMQ Kafka Spark Slack PagerDuty JSON Microservices Cassandra ElasticSearch Sensu Service Discovery Honeypot Kubernetes PostGreSQL Druid Flink Launch Darkly Chef Consul Terraform CloudFormation The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary The data that is used in financial markets is time oriented and multidimensional, which makes it difficult to manage in either relational or timeseries databases. To make this information more manageable the team at Alapaca built a new data store specifically for retrieving and analyzing data generated by trading markets. In this episode Hitoshi Harada, the CTO of Alapaca, and Christopher Ryan, their lead software engineer, explain their motivation for building MarketStore, how it operates, and how it has helped to simplify their development workflows. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Christopher Ryan and Hitoshi Harada about MarketStore, a storage server for large volumes of financial timeseries data Interview Introduction How did you get involved in the area of data management? What was your motivation for creating MarketStore? What are the characteristics of financial time series data that make it challenging to manage? What are some of the workflows that MarketStore is used for at Alpaca and how were they managed before it was available? With MarketStore’s data coming from multiple third party services, how are you managing to keep the DB up-to-date and in sync with those services? What is the worst case scenario if there is a total failure in the data store? What guards have you built to prevent such a situation from occurring? Since MarketStore is used for querying and analyzing data having to do with financial markets and there are potentially large quantities of money being staked on the results of that analysis, how do you ensure that the operations being performed in MarketStore are accurate and repeatable? What were the most challenging aspects of building MarketStore and integrating it into the rest of your systems? Motivation for open sourcing the code? What is the next planned major feature for MarketStore, and what use-case is it aiming to support? Contact Info Christopher Email Hitoshi Email Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links MarketStore GitHub Release Announcement Alpaca IBM DB2 GreenPlum Algorithmic Trading Backtesting OHLC (Open-High-Low-Close) HDF5 Golang C++ Timeseries Database List InfluxDB JSONRPC Slait CircleCI GDAX The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Search is a common requirement for applications of all varieties. Elasticsearch was built to make it easy to include search functionality in projects built in any language. From that foundation, the rest of the Elastic Stack has been built, expanding to many more use cases in the proces. In this episode Philipp Krenn describes the various pieces of the stack, how they fit together, and how you can use them in your infrastructure to store, search, and analyze your data. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Philipp Krenn about the Elastic Stack and the ways that you can use it in your systems Interview Introduction How did you get involved in the area of data management? The Elasticsearch product has been around for a long time and is widely known, but can you give a brief overview of the other components that make up the Elastic Stack and how they work together? Beyond the common pattern of using Elasticsearch as a search engine connected to a web application, what are some of the other use cases for the various pieces of the stack? What are the common scaling bottlenecks that users should be aware of when they are dealing with large volumes of data? What do you consider to be the biggest competition to the Elastic Stack as you expand the capabilities and target usage patterns? What are the biggest challenges that you are tackling in the Elastic stack, technical or otherwise? What are the biggest challenges facing Elastic as a company in the near to medium term? Open source as a business model: https://www.elastic.co/blog/doubling-down-on-open?utm_source=rss&utm_medium=rss What is the vision for Elastic and the Elastic Stack going forward and what new features or functionality can we look forward to? Contact Info @xeraa on Twitter xeraa on GitHub Website Email Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Elastic Vienna – Capital of Austria What Is Developer Advocacy? NoSQL MongoDB Elasticsearch Cassandra Neo4J Hazelcast Apache Lucene Logstash Kibana Beats X-Pack ELK Stack Metrics APM (Application Performance Monitoring) GeoJSON Split Brain Elasticsearch Ingest Nodes PacketBeat Elastic Cloud Elasticon Kibana Canvas SwiftType The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary As software lifecycles move faster, the database needs to be able to keep up. Practices such as version controlled migration scripts and iterative schema evolution provide the necessary mechanisms to ensure that your data layer is as agile as your application. Pramod Sadalage saw the need for these capabilities during the early days of the introduction of modern development practices and co-authored a book to codify a large number of patterns to aid practitioners, and in this episode he reflects on the current state of affairs and how things have changed over the past 12 years. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Pramod Sadalage about refactoring databases and integrating database design into an iterative development workflow Interview Introduction How did you get involved in the area of data management? You first co-authored Refactoring Databases in 2006. What was the state of software and database system development at the time and why did you find it necessary to write a book on this subject? What are the characteristics of a database that make them more difficult to manage in an iterative context? How does the practice of refactoring in the context of a database compare to that of software? How has the prevalence of data abstractions such as ORMs or ODMs impacted the practice of schema design and evolution? Is there a difference in strategy when refactoring the data layer of a system when using a non-relational storage system? How has the DevOps movement and the increased focus on automation affected the state of the art in database versioning and evolution? What have you found to be the most problematic aspects of databases when trying to evolve the functionality of a system? Looking back over the past 12 years, what has changed in the areas of database design and evolution? How has the landscape of tooling for managing and applying database versioning changed since you first wrote Refactoring Databases? What do you see as the biggest challenges facing us over the next few years? Contact Info Website pramodsadalage on GitHub @pramodsadalage on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Database Refactoring Website Book Thoughtworks Martin Fowler Agile Software Development XP (Extreme Programming) Continuous Integration The Book Wikipedia Test First Development DDL (Data Definition Language) DML (Data Modification Language) DevOps Flyway Liquibase DBMaintain Hibernate SQLAlchemy ORM (Object Relational Mapper) ODM (Object Document Mapper) NoSQL Document Database MongoDB OrientDB CouchBase CassandraDB Neo4j ArangoDB Unit Testing Integration Testing OLAP (On-Line Analytical Processing) OLTP (On-Line Transaction Processing) Data Warehouse Docker QA==Quality Assurance HIPAA (Health Insurance Portability and Accountability Act) PCI DSS (Payment Card Industry Data Security Standard) Polyglot Persistence Toplink Java ORM Ruby on Rails ActiveRecord Gem The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Data is an increasingly sought after raw material for business in the modern economy. One of the factors driving this trend is the increase in applications for machine learning and AI which require large quantities of information to work from. As the demand for data becomes more widespread the market for providing it will begin transform the ways that information is collected and shared among and between organizations. With his experience as a chair for the O’Reilly AI conference and an investor for data driven businesses Roger Chen is well versed in the challenges and solutions being facing us. In this episode he shares his perspective on the ways that businesses can work together to create shared data resources that will allow them to reduce the redundancy of their foundational data and improve their overall effectiveness in collecting useful training sets for their particular products. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers A few announcements: The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register. Your host is Tobias Macey and today I’m interviewing Roger Chen about data liquidity and its impact on our future economies Interview Introduction How did you get involved in the area of data management? You wrote an essay discussing how the increasing usage of machine learning and artificial intelligence applications will result in a demand for data that necessitates what you refer to as ‘Data Liquidity’. Can you explain what you mean by that term? What are some examples of the types of data that you envision as being foundational to multiple organizations and problem domains? Can you provide some examples of the structures that could be created to facilitate data sharing across organizational boundaries? Many companies view their data as a strategic asset and are therefore loathe to provide access to other individuals or organizations. What encouragement can you provide that would convince them to externalize any of that information? What kinds of storage and transmission infrastructure and tooling are necessary to allow for wider distribution of, and collaboration on, data assets? What do you view as being the privacy implications from creating and sharing these larger pools of data inventory? What do you view as some of the technical challenges associated with identifying and separating shared data from those that are specific to the business model of the organization? With broader access to large data sets, how do you anticipate that impacting the types of businesses or products that are possible for smaller organizations? Contact Info @rgrchen on Twitter LinkedIn Angel List Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Electrical Engineering Berkeley Silicon Nanophotonics Data Liquidity In The Age Of Inference Data Silos Example of a Data Commons Cooperative Google Maps Moat: An article describing how Google Maps has refined raw data to create a new product Genomics Phenomics ImageNet Open Data Data Brokerage Smart Contracts IPFS Dat Protocol Homomorphic Encryption FileCoin Data Programming Snorkel Website Podcast Interview The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary One of the sources of data that often gets overlooked is the systems that we use to run our businesses. This data is not used to directly provide value to customers or understand the functioning of the business, but it is still a critical component of a successful system. Sam Stokes is an engineer at Honeycomb where he helps to build a platform that is able to capture all of the events and context that occur in our production environments and use them to answer all of your questions about what is happening in your system right now. In this episode he discusses the challenges inherent in capturing and analyzing event data, the tools that his team is using to make it possible, and how this type of knowledge can be used to improve your critical infrastructure. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers A few announcements: There is still time to register for the O’Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20% The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register. Your host is Tobias Macey and today I’m interviewing Sam Stokes about his work at Honeycomb, a modern platform for observability of software systems Interview Introduction How did you get involved in the area of data management? What is Honeycomb and how did you get started at the company? Can you start by giving an overview of your data infrastructure and the path that an event takes from ingest to graph? What are the characteristics of the event data that you are dealing with and what challenges does it pose in terms of processing it at scale? In addition to the complexities of ingesting and storing data with a high degree of cardinality, being able to quickly analyze it for customer reporting poses a number of difficulties. Can you explain how you have built your systems to facilitate highly interactive usage patterns? A high degree of visibility into a running system is desirable for developers and systems adminstrators, but they are not always willing or able to invest the effort to fully instrument the code or servers that they want to track. What have you found to be the most difficult aspects of data collection, and do you have any tooling to simplify the implementation for user? How does Honeycomb compare to other systems that are available off the shelf or as a service, and when is it not the right tool? What have been some of the most challenging aspects of building, scaling, and marketing Honeycomb? Contact Info @samstokes on Twitter Blog samstokes on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Honeycomb Retriever Monitoring and Observability Kafka Column Oriented Storage Elasticsearch Elastic Stack Django Ruby on Rails Heroku Kubernetes Launch Darkly Splunk Datadog Cynefin Framework Go-Lang Terraform AWS The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary The responsibilities of a data scientist and a data engineer often overlap and occasionally come to cross purposes. Despite these challenges it is possible for the two roles to work together effectively and produce valuable business outcomes. In this episode Will McGinnis discusses the opinions that he has gained from experience on how data teams can play to their strengths to the benefit of all. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers A few announcements: There is still time to register for the O’Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20% The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register. Your host is Tobias Macey and today I’m interviewing Will McGinnis about the relationship and boundaries between data engineers and data scientists Interview Introduction How did you get involved in the area of data management? The terms “Data Scientist” and “Data Engineer” are fluid and seem to have a different meaning for everyone who uses them. Can you share how you define those terms? What parallels do you see between the relationships of data engineers and data scientists and those of developers and systems administrators? Is there a particular size of organization or problem that serves as a tipping point for when you start to separate the two roles into the responsibilities of more than one person or team? What are the benefits of splitting the responsibilities of data engineering and data science? What are the disadvantages? What are some strategies to ensure successful interaction between data engineers and data scientists? How do you view these roles evolving as they become more prevalent across companies and industries? Contact Info Website wdm0006 on GitHub @willmcginniser on Twitter LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Blog Post: Tendencies of Data Engineers and Data Scientists Predikto Categorical Encoders DevOps SciKit-Learn The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary As communications between machines become more commonplace the need to store the generated data in a time-oriented manner increases. The market for timeseries data stores has many contenders, but they are not all built to solve the same problems or to scale in the same manner. In this episode the founders of TimescaleDB, Ajay Kulkarni and Mike Freedman, discuss how Timescale was started, the problems that it solves, and how it works under the covers. They also explain how you can start using it in your infrastructure and their plans for the future. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I&#8217;m interviewing Ajay Kulkarni and Mike Freedman about Timescale DB, a scalable timeseries database built on top of PostGreSQL Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Timescale is and how the project got started? The landscape of time series databases is extensive and oftentimes difficult to navigate. How do you view your position in that market and what makes Timescale stand out from the other options? In your blog post that explains the design decisions for how Timescale is implemented you call out the fact that the inserted data is largely append only which simplifies the index management. How does Timescale handle out of order timestamps, such as from infrequently connected sensors or mobile devices? How is Timescale implemented and how has the internal architecture evolved since you first started working on it? What impact has the 10.0 release of PostGreSQL had on the design of the project? Is timescale compatible with systems such as Amazon RDS or Google Cloud SQL? For someone who wants to start using Timescale what is involved in deploying and maintaining it? What are the axes for scaling Timescale and what are the points where that scalability breaks down? Are you aware of anyone who has deployed it on top of Citus for scaling horizontally across instances? What has been the most challenging aspect of building and marketing Timescale? When is Timescale the wrong tool to use for time series data? One of the use cases that you call out on your website is for systems metrics and monitoring. How does Timescale fit into that ecosystem and can it be used along with tools such as Graphite or Prometheus? What are some of the most interesting uses of Timescale that you have seen? Which came first, Timescale the business or Timescale the database, and what is your strategy for ensuring that the open source project and the company around it both maintain their health? What features or improvements do you have planned for future releases of Timescale? Contact Info Ajay LinkedIn @acoustik on Twitter Timescale Blog Mike Website LinkedIn @michaelfreedman on Twitter Timescale Blog Timescale Website @timescaledb on Twitter GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Timescale PostGreSQL Citus Timescale Design Blog Post MIT NYU Stanford SDN Princeton Machine Data Timeseries Data List of Timeseries Databases NoSQL Online Transaction Processing (OLTP) Object Relational Mapper (ORM) Grafana Tableau Kafka When Boring Is Awesome PostGreSQL RDS Google Cloud SQL Azure DB Docker Continuous Aggregates Streaming Replication PGPool II Kubernetes Docker Swarm Citus Data Website Data Engineering Podcast Interview Database Indexing B-Tree Index GIN Index GIST Index STE Energy Redis Graphite Prometheus pg_prometheus OpenMetrics Standard Proposal Timescale Parallel Copy Hadoop PostGIS KDB+ DevOps Internet of Things MongoDB Elastic DataBricks Apache Spark Confluent New Enterprise Associates MapD Benchmark Ventures Hortonworks 2σ Ventures CockroachDB Cloudflare EMC Timescale Blog: Why SQL is beating NoSQL, and what this means for the future of data The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug?utm_source=rss&utm_medium=rss" target="_blank"…

Summary One of the critical components for modern data infrastructure is a scalable and reliable messaging system. Publish-subscribe systems have been popular for many years, and recently stream oriented systems such as Kafka have been rising in prominence. This week Rajan Dhabalia and Matteo Merli discuss the work they have done on Pulsar, which supports both options, in addition to being globally scalable and fast. They explain how Pulsar is architected, how to scale it, and how it fits into your existing infrastructure. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers A few announcements: There is still time to register for the O&#8217;Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20% The O&#8217;Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register. Your host is Tobias Macey and today I&#8217;m interviewing Rajan Dhabalia and Matteo Merli about Pulsar, a distributed open source pub-sub messaging system Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Pulsar is and what the original inspiration for the project was? What have been some of the most challenging aspects of building and promoting Pulsar? For someone who wants to run Pulsar, what are the infrastructure and network requirements that they should be considering and what is involved in deploying the various components? What are the scaling factors for Pulsar and what aspects of deployment and administration should users pay special attention to? What projects or services do you consider to be competitors to Pulsar and what makes it stand out in comparison? The documentation mentions that there is an API layer that provides drop-in compatibility with Kafka. Does that extend to also supporting some of the plugins that have developed on top of Kafka? One of the popular aspects of Kafka is the persistence of the message log, so I&#8217;m curious how Pulsar manages long-term storage and reprocessing of messages that have already been acknowledged? When is Pulsar the wrong tool to use? What are some of the improvements or new features that you have planned for the future of Pulsar? Contact Info Matteo merlimat on GitHub @merlimat on Twitter Rajan @dhabaliaraj on Twitter rhabalia on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Pulsar Publish-Subscribe Yahoo Streamlio ActiveMQ Kafka Bookkeeper SLA (Service Level Agreement) Write-Ahead Log Ansible Zookeeper Pulsar Deployment Instructions RabbitMQ Confluent Schema Registry Podcast Interview Kafka Connect Wallaroo Podcast Interview Kinesis Athenz The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Sharing data across multiple computers, particularly when it is large and changing, is a difficult problem to solve. In order to provide a simpler way to distribute and version data sets among collaborators the Dat Project was created. In this episode Danielle Robinson and Joe Hand explain how the project got started, how it functions, and some of the many ways that it can be used. They also explain the plans that the team has for upcoming features and uses that you can watch out for in future releases. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers A few announcements: There is still time to register for the O&#8217;Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20% The O&#8217;Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register. Your host is Tobias Macey and today I&#8217;m interviewing Danielle Robinson and Joe Hand about Dat Project, a distributed data sharing protocol for building applications of the future Interview Introduction How did you get involved in the area of data management? What is the Dat project and how did it get started? How have the grants to the Dat project influenced the focus and pace of development that was possible? Now that you have established a non-profit organization around Dat, what are your plans to support future sustainability and growth of the project? Can you explain how the Dat protocol is designed and how it has evolved since it was first started? How does Dat manage conflict resolution and data versioning when replicating between multiple machines? One of the primary use cases that is mentioned in the documentation and website for Dat is that of hosting and distributing open data sets, with a focus on researchers. How does Dat help with that effort and what improvements does it offer over other existing solutions? One of the difficult aspects of building a peer-to-peer protocol is that of establishing a critical mass of users to add value to the network. How have you approached that effort and how much progress do you feel that you have made? How does the peer-to-peer nature of the platform affect the architectural patterns for people wanting to build applications that are delivered via dat, vs the common three-tier architecture oriented around persistent databases? What mechanisms are available for content discovery, given the fact that Dat URLs are private and unguessable by default? For someone who wants to start using Dat today, what is involved in creating and/or consuming content that is available on the network? What have been the most challenging aspects of building and promoting Dat? What are some of the most interesting or inspiring uses of the Dat protocol that you are aware of? Contact Info Dat datproject.org Email @dat_project on Twitter Dat Chat Danielle Email @daniellecrobins Joe Email @joeahand on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Dat Project Code For Science and Society Neuroscience Cell Biology OpenCon Mozilla Science Open Education Open Access Open Data Fortune 500 Data Warehouse Knight Foundation Alfred P. Sloan F

Summary The majority of the conversation around machine learning and big data pertains to well-structured and cleaned data sets. Unfortunately, that is just a small percentage of the information that is available, so the rest of the sources of knowledge in a company are housed in so-called &#8220;Dark Data&#8221; sets. In this episode Alex Ratner explains how the work that he and his fellow researchers are doing on Snorkel can be used to extract value by leveraging labeling functions written by domain experts to generate training sets for machine learning models. He also explains how this approach can be used to democratize machine learning by making it feasible for organizations with smaller data sets than those required by most tooling. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I&#8217;m interviewing Alex Ratner about Snorkel and Dark Data Interview Introduction How did you get involved in the area of data management? Can you start by sharing your definition of dark data and how Snorkel helps to extract value from it? What are some of the most challenging aspects of building labelling functions and what tools or techniques are available to verify their validity and effectiveness in producing accurate outcomes? Can you provide some examples of how Snorkel can be used to build useful models in production contexts for companies or problem domains where data collection is difficult to do at large scale? For someone who wants to use Snorkel, what are the steps involved in processing the source data and what tooling or systems are necessary to analyse the outputs for generating usable insights? How is Snorkel architected and how has the design evolved over its lifetime? What are some situations where Snorkel would be poorly suited for use? What are some of the most interesting applications of Snorkel that you are aware of? What are some of the other projects that you and your group are working on that interact with Snorkel? What are some of the features or improvements that you have planned for future releases of Snorkel? Contact Info Website ajratner on Github @ajratner on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Stanford DAWN HazyResearch Snorkel Christopher Ré Dark Data DARPA Memex Training Data FDA ImageNet National Library of Medicine Empirical Studies of Conflict Data Augmentation PyTorch Tensorflow Generative Model Discriminative Model Weak Supervision The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary As we scale our systems to handle larger volumes of data, geographically distributed users, and varied data sources the requirement to distribute the computational resources for managing that information becomes more pronounced. In order to ensure that all of the distributed nodes in our systems agree with each other we need to build mechanisms to properly handle replication of data and conflict resolution. In this episode Christopher Meiklejohn discusses the research he is doing with Conflict-Free Replicated Data Types (CRDTs) and how they fit in with existing methods for sharing and sharding data. He also shares resources for systems that leverage CRDTs, how you can incorporate them into your systems, and when they might not be the right solution. It is a fascinating and informative treatment of a topic that is becoming increasingly relevant in a data driven world. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I&#8217;m interviewing Christopher Meiklejohn about establishing consensus in distributed systems Interview Introduction How did you get involved in the area of data management? You have dealt with CRDTs with your work in industry, as well as in your research. Can you start by explaining what a CRDT is, how you first began working with them, and some of their current manifestations? Other than CRDTs, what are some of the methods for establishing consensus across nodes in a system and how does increased scale affect their relative effectiveness? One of the projects that you have been involved in which relies on CRDTs is LASP. Can you describe what LASP is and what your role in the project has been? Can you provide examples of some production systems or available tools that are leveraging CRDTs? If someone wants to take advantage of CRDTs in their applications or data processing, what are the available off-the-shelf options, and what would be involved in implementing custom data types? What areas of research are you most excited about right now? Given that you are currently working on your PhD, do you have any thoughts on the projects or industries that you would like to be involved in once your degree is completed? Contact Info Website cmeiklejohn on GitHub Google Scholar Citations Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Basho Riak Syncfree LASP CRDT Mesosphere CAP Theorem Cassandra DynamoDB Bayou System (Xerox PARC) Multivalue Register Paxos RAFT Byzantine Fault Tolerance Two Phase Commit Spanner ReactiveX Tensorflow Erlang Docker Kubernetes Erleans Orleans Atom Editor Automerge Martin Klepman Akka Delta CRDTs Antidote DB Kops Eventual Consistency Causal Consistency ACID Transactions Joe Hellerstein The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary PostGreSQL has become one of the most popular and widely used databases, and for good reason. The level of extensibility that it supports has allowed it to be used in virtually every environment. At Citus Data they have built an extension to support running it in a distributed fashion across large volumes of data with parallelized queries for improved performance. In this episode Ozgun Erdogan, the CTO of Citus, and Craig Kerstiens, Citus Product Manager, discuss how the company got started, the work that they are doing to scale out PostGreSQL, and how you can start using it in your environment. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I&#8217;m interviewing Ozgun Erdogan and Craig Kerstiens about Citus, worry free PostGreSQL Interview Introduction How did you get involved in the area of data management? Can you describe what Citus is and how the project got started? Why did you start with Postgres vs. building something from the ground up? What was the reasoning behind converting Citus from a fork of PostGres to being an extension and releasing an open source version? How well does Citus work with other Postgres extensions, such as PostGIS, PipelineDB, or Timescale? How does Citus compare to options such as PostGres-XL or the Postgres compatible Aurora service from Amazon? How does Citus operate under the covers to enable clustering and replication across multiple hosts? What are the failure modes of Citus and how does it handle loss of nodes in the cluster? For someone who is interested in migrating to Citus, what is involved in getting it deployed and moving the data out of an existing system? How do the different options for leveraging Citus compare to each other and how do you determine which features to release or withhold in the open source version? Are there any use cases that Citus enables which would be impractical to attempt in native Postgres? What have been some of the most challenging aspects of building the Citus extension? What are the situations where you would advise against using Citus? What are some of the most interesting or impressive uses of Citus that you have seen? What are some of the features that you have planned for future releases of Citus? Contact Info Citus Data citusdata.com @citusdata on Twitter citusdata on GitHub Craig Email Website @craigkerstiens on Twitter Ozgun Email ozgune on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Citus Data PostGreSQL NoSQL Timescale SQL blog post PostGIS PostGreSQL Graph Database JSONB Data Type PipelineDB Timescale PostGres-XL Aurora PostGres Amazon RDS Streaming Replication CitusMX CTE (Common Table Expression) HipMunk Citus Sharding Blog Post Wal-e Wal-g Heap Analytics HyperLogLog C-Store The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Data oriented applications that need to operate on large, fast-moving sterams of information can be difficult to build and scale due to the need to manage their state. In this episode Sean T. Allen, VP of engineering for Wallaroo Labs, explains how Wallaroo was designed and built to reduce the cognitive overhead of building this style of project. He explains the motivation for building Wallaroo, how it is implemented, and how you can start using it today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I&#8217;m interviewing Sean T. Allen about Wallaroo, a framework for building and operating stateful data applications at scale Interview Introduction How did you get involved in the area of data engineering? What is Wallaroo and how did the project get started? What is the Pony language, and what features does it have that make it well suited for the problem area that you are focusing on? Why did you choose to focus first on Python as the language for interacting with Wallaroo and how is that integration implemented? How is Wallaroo architected internally to allow for distributed state management? Is the state persistent, or is it only maintained long enough to complete the desired computation? If so, what format do you use for long term storage of the data? What have been the most challenging aspects of building the Wallaroo platform? Which axes of the CAP theorem have you optimized for? For someone who wants to build an application on top of Wallaroo, what is involved in getting started? Once you have a working application, what resources are necessary for deploying to production and what are the scaling factors? What are the failure modes that users of Wallaroo need to account for in their application or infrastructure? What are some situations or problem types for which Wallaroo would be the wrong choice? What are some of the most interesting or unexpected uses of Wallaroo that you have seen? What do you have planned for the future of Wallaroo? Contact Info IRC Mailing List Wallaroo Labs Twitter Email Personal Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Wallaroo Labs Storm Applied Apache Storm Risk Analysis Pony Language Erlang Akka Tail Latency High Performance Computing Python Apache Software Foundation Beyond Distributed Transactions: An Apostate&#8217;s View Consistent Hashing Jepsen Lineage Driven Fault Injection Chaos Engineering QCon 2016 Talk Codemesh in London: How did I get here? CAP Theorem CRDT Sync Free Project Basho Wallaroo on GitHub Docker Puppet Chef Ansible SaltStack Kafka TCP Dask Data Engineering Episode About Dask Beowulf Cluster Redis Flink Haskell The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast

Summary Time series databases have long been the cornerstone of a robust metrics system, but the existing options are often difficult to manage in production. In this episode Jeroen van der Heijden explains his motivation for writing a new database, SiriDB, the challenges that he faced in doing so, and how it works under the hood. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you&#8217;re ready to launch your next project you&#8217;ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I&#8217;m interviewing Jeroen van der Heijden about SiriDB, a next generation time series database Interview Introduction How did you get involved in the area of data engineering? What is SiriDB and how did the project get started? What was the inspiration for the name? What was the landscape of time series databases at the time that you first began work on Siri? How does Siri compare to other time series databases such as InfluxDB, Timescale, KairosDB, etc.? What do you view as the competition for Siri? How is the server architected and how has the design evolved over the time that you have been working on it? Can you describe how the clustering mechanism functions? Is it possible to create pools with more than two servers? What are the failure modes for SiriDB and where does it fall on the spectrum for the CAP theorem? In the documentation it mentions needing to specify the retention period for the shards when creating a database. What is the reasoning for that and what happens to the individual metrics as they age beyond that time horizon? One of the common difficulties when using a time series database in an operations context is the need for high cardinality of the metrics. How are metrics identified in Siri and is there any support for tagging? What have been the most challenging aspects of building Siri? In what situations or environments would you advise against using Siri? Contact Info joente on Github LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links SiriDB Oversight InfluxDB LevelDB OpenTSDB Timescale DB KairosDB Write Ahead Log Grafana The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast