Summary Data assets and the pipelines that create them have become critical production infrastructure for companies. This adds a requirement for reliability and management of up-time similar to application infrastructure. In this episode Francisco Alberini and Mei Tao share their insights on what incident management looks like for data platforms and the teams that support them.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now! Your host is Tobias Macey and today I’m interviewing Francisco Alberini and Mei Tao about patterns and practices for incident management in data teams

Interview

Introduction How did you get involved in the area of data management? Can you start by describing some of the ways that an "incident" can manifest in a data system?

At a high level, what are the steps and participants required to bring an incident to resolution?

The principle of incident management is familiar to application/site reliability teams. What is the current state of the art/adoption for these practices among data teams? What are the signals that teams should be monitoring to identify and alert on potential incidents?

Alerting is a subjective and nuanced practice, regardless of the context. What are some useful practices that you have seen and enacted to reduce alert fatigue

Summary Data observability is a term that has been co-opted by numerous vendors with varying ideas of what it should mean. At Acceldata, they view it as a holistic approach to understanding the computational and logical elements that power your analytical capabilities. In this episode Tristan Spaulding, head of product at Acceldata, explains the multi-dimensional nature of gaining visibility into your running data platform and how they have architected their platform to assist in that endeavor.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. TimescaleDB, from your friends at Timescale, is the leading open-source relational database with support for time-series data. Time-series data is time stamped so you can measure how a system is changing. Time-series data is relentless and requires a database like TimescaleDB with speed and petabyte-scale. Understand the past, monitor the present, and predict the future. That’s Timescale. Visit them today at dataengineeringpodcast.com/timescale Your host is Tobias Macey and today I’m interviewing Tristan Spaulding about Acceldata, a platform offering multidimensional data observability for modern data infrastructure

Interview

Introduction How did you get involved in the area of data? Can you describe what Acceldata is and the story behind it? What does it mean for a data observability platform to be "multidimensional"? How do the architectural characteristics of the "modern data stack" influence the requirements and implementation of data observability strategies? The data observability ecosystem has seen a lot of activity over the past ~2-3 years. What are the unique capabilities/use cases that Acceldata supports? Who are your target users and how does that focus influence the way that you have approached feature and design priorities? What are some of the ways that you are using the Acceldata platform to run Acceldata? Can you describe how the Acceldata platform is implemented?

How have the design and goals of the system changed or evolved since you started working on it?

How are you man

Summary When you think about selecting a database engine for your project you typically consider options focused on serving multiple concurrent users. Sometimes what you really need is an embedded database that is blazing fast for single user workloads. DuckDB is an in-process database engine optimized for OLAP applications to speed up your analytical queries that meets you where you are, whether that’s Python, R, Java, even the web. In this episode, Hannes Mühleisen, co-creator and CEO of DuckDB Labs, shares the motivations for creating the project, the myriad ways that it can be used to speed up your data projects, and the detailed engineering efforts that go into making it adaptable to any environment. This is a fascinating and humorous exploration of a truly useful piece of technology.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Your host is Tobias Macey and today I’m interviewing Hannes Mühleisen about DuckDB, an in-process embedded database engine for columnar analytics

Interview

Introduction How did you get involved in the area of data management? Can you describe what DuckDB is and the story behind it? Where did the name come from? What are some of the use cases that DuckDB is designed to support? The interface for DuckDB is similar (at least in spirit) to SQLite. What are the deciding factors for when to use one vs. the other?

How might they be used in concert to take advantage of their relative strengths?

What are some of the ways that DuckDB can be used to better effect than options provided by different language ecosystems? Can you describe how DuckDB is implemented?

How has the design and goals of the project changed or evolved since you began working on it? What are some of the optimizations that you have had to make in order to support performant access to data that exceeds available memory?

Can you describe a typical workflow of incorporating DuckDB into an analytical project? What are some of the libraries/tools/systems that DuckDB might replace in the scope of a project or team? What are some of the

Summary Building a data platform is a complex journey that requires a significant amount of planning to do well. It requires knowledge of the available technologies, the requirements of the operating environment, and the expectations of the stakeholders. In this episode Tobias Macey, the host of the show, reflects on his plans for building a data platform and what he has learned from running the podcast that is influencing his choices.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription TimescaleDB, from your friends at Timescale, is the leading open-source relational database with support for time-series data. Time-series data is time stamped so you can measure how a system is changing. Time-series data is relentless and requires a database like TimescaleDB with speed and petabyte-scale. Understand the past, monitor the present, and predict the future. That’s Timescale. Visit them today at dataengineeringpodcast.com/timescale RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. I’m your host, Tobias Macey, and today I’m sharing the approach that I’m taking while designing a data platform

Interview

Introduction How did you get involved in the area of data management? What are the components that need to be considered when designing a solution?

Data integration (extract and load)

What are your data sources? Batch or streaming (acceptable latencies)

Data storage (lake or warehouse)

How is the data going to be used? What other tools/systems will need to integrate with it? The warehouse (Bigquery, Snowflake, Redshift) has become the focal point of the "modern data stack"

Data orchestration

Who will be managing the workflow logic?

Metadata repository

Types of metadata (catalog, lineage, access, queries, etc.)

Semantic layer/reporting Data applications

Implementation phases

Build a single end-to-end workflow of a data application using a single category of data across sources Validate the ability for an analyst/data scientist to self-serve a notebook powered analysis Iterate

Risks/unknowns

Data modeling requirements Specific implementation details as integrations acros

Summary Python has grown to be one of the top languages used for all aspects of data, from collection and cleaning, to analysis and machine learning. Along with that growth has come an explosion of tools and engines that help power these workflows, which introduces a great deal of complexity when scaling from single machines and exploratory development to massively parallel distributed computation. In answer to that challenge the Fugue project offers an interface to automatically translate across Pandas, Spark, and Dask execution environments without having to modify your logic. In this episode core contributor Kevin Kho explains how the slight differences in the underlying engines can lead to big problems, how Fugue works to hide those differences from the developer, and how you can start using it in your own work today.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Every data project starts with collecting the information that will provide answers to your questions or inputs to your models. The web is the largest trove of information on the planet and Oxylabs helps you unlock its potential. With the Oxylabs scraper APIs you can extract data from even javascript heavy websites. Combined with their residential proxies you can be sure that you’ll have reliable and high quality data whenever you need it. Go to dataengineeringpodcast.com/oxylabs today and use code DEP25 to get your special discount on residential proxies. Your host is Tobias Macey and today I’m interviewing Kevin Kho about Fugue, a library that offers a unified interface for distributed computing that lets users execute Python, pandas, and SQL code on Spark and Dask without rewrites

Interview

Introduction How did you get involved in the area of data management? Can you describe what Fugue is and the story behind it? What are the core goals of the Fugue project? Who are the target users for Fugue and how does that influence the feature priorities and API design? How does Fugue compare to projects such as Modin, etc. for abst

Summary The life sciences as an industry has seen incredible growth in scale and sophistication, along with the advances in data technology that make it possible to analyze massive amounts of genomic information. In this episode Guy Yachdav, director of software engineering for ImmunAI, shares the complexities that are inherent to managing data workflows for bioinformatics. He also explains how he has architected the systems that ingest, process, and distribute the data that he is responsible for and the requirements that are introduced when collaborating with researchers, domain experts, and machine learning developers.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. Your host is Tobias Macey and today I’m interviewing Guy Yachdav, Director of Software Engineering at Immunai, about his work at Immunai to wrangle biological data for advancing research into the human immune system.

Interview

Introduction (see Guy’s bio below) How did you get involved in the area of data management? Can you describe what Immunai is and the story behind it? What are some of the categories of information that you are working with?

What kinds of insights are you trying to power/questions that you are trying to answer with that data?

Who are the stakeholders that you are working with and how does that influence your approach to the integration/transformation/presentation of the data? What are some of the challenges unique to the biological data domain that you have had to address?

What are some of the limitations in the off-the-shelf tools when applied to biological data? How have you approached the selection of tools/techniques/technologies to make your work maintainable for your engineers and accessible for your end users?

Can

Summary Streaming data sources are becoming more widely available as tools to handle their storage and distribution mature. However it is still a challenge to analyze this data as it arrives, while supporting integration with static data in a unified syntax. Deephaven is a project that was designed from the ground up to offer an intuitive way for you to bring your code to your data, whether it is streaming or static without having to know which is which. In this episode Pete Goddard, founder and CEO of Deephaven shares his journey with the technology that powers the platform, how he and his team are pouring their energy into the community edition of the technology so that you can use it freely in your own work.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m interviewing Pete Goddard about his work at Deephaven, a query engine optimized for manipulating and merging streaming and static data

Interview

Introduction How did you get involved in the area of data management? Can you describe what Deephaven is and the story behind it? What is the role of Deephaven in the context of an organization’s data platform?

What are the upstream and downstream systems and teams that it is likely to be integrated with?

Who are the target users of Deephaven and how does that influence the feature priorities and design of the platform? comparison of use cases/experience with Materialize What are the different components that comprise the suite of functionality in Deephaven? How have you architected the system?

What are some of the ways t

Summary Along with globalization of our societies comes the need to analyze the geospatial and geotemporal data that is needed to manage the growth in commerce, communications, and other activities. In order to make geospatial analytics more maintainable and scalable there has been an increase in the number of database engines that provide extensions to their SQL syntax that supports manipulation of spatial data. In this episode Matthew Forrest shares his experiences of working in the domain of geospatial analytics and the application of SQL dialects to his analysis.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m interviewing Matthew Forrest about doing spatial analysis in SQL

Interview

Introduction How did you get involved in the area of data management? Can you describe what spatial SQL is and some of the use cases that it is relevant for? compatibility with/comparison to syntax from PostGIS What is involved in implementation of spatial logic in database engines mapping geospatial concepts into declarative syntax foundational data types data modeling workflow for analyzing spatial data sets outside of database engines translating from e.g. geopandas to SQL level of support in database engines for spatial data types What are the most interesting, innovative, or unexpected ways that you have seen spatial SQL used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with spatial SQL? When is SQL the wrong choice for spatial analysis? What do you have planned for the future o

Summary The Data Engineering Podcast has been going for five years now and has included conversations and interviews with a huge number of guests, covering a broad range of topics. In addition to that, the host curated the essays contained in the book "97 Things Every Data Engineer Should Know", using the knowledge and context gained from running the show to inform the selection process. In this episode he shares some reflections on producing the podcast, compiling the book, and relevant trends in the ecosystem of data engineering. He also provides some advice for those who are early in their career of data engineering and looking to advance in their roles.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m doing something a bit different. I’m going to talk about some of the lessons that I have learned while running the podcast, compiling the book "97 Things Every Data Engineer Should Know", and some of the themes that I’ve observed throughout.

Interview

Introduction How did you get involved in the area of data management? Overview of the 97 things book

How the project came about Goals of the book

What are the paths into data engineering? What are some of the macroscopic themes in the industry? What are some of the microscopic details that are useful/necessary to succeed as a data engineer? What are some of the career/team/organizational details that are helpful for data engineers? What are the most interesting, innovative, or unexpected outcomes/feedback that I have seen from running the podcast and working on the book

Summary Data platforms are exemplified by a complex set of connections that are subject to a set of constantly evolving requirements. In order to make this a tractable problem it is necessary to define boundaries for communication between concerns, which brings with it the need to establish interface contracts for communicating across those boundaries. The recent move toward the data mesh as a formalized architecture that builds on this design provides the language that data teams need to make this a more organized effort. In this episode Abhi Sivasailam shares his experience designing and implementing a data mesh solution with his team at Flexport, and the importance of defining and enforcing data contracts that are implemented at those domain boundaries.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m interviewing Abhi Sivasailam about the different social and technical interfaces available for defining and enforcing data contracts

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what your working definition of a "data contract" is?

What are the goals and purpose of these contracts?

What are the locations and methods of defining a data contract?

What kind of information needs to be encoded in a contract definition?

How do you manage enforcement of contracts? manifestations of contracts in data mesh implementation ergonomics (technical and social) of data contracts and how to prevent them from prohibiting productivity What are the most interesting, innovative

Summary Data quality control is a requirement for being able to trust the various reports and machine learning models that are relying on the information that you curate. Rules based systems are useful for validating known requirements, but with the scale and complexity of data in modern organizations it is impractical, and often impossible, to manually create rules for all potential errors. The team at Anomalo are building a machine learning powered platform for identifying and alerting on anomalous and invalid changes in your data so that you aren’t flying blind. In this episode founders Elliot Shmukler and Jeremy Stanley explain how they have architected the system to work with your data warehouse and let you know about the critical issues hiding in your data without overwhelming you with alerts.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Elliot Shmukler and Jeremy Stanley about Anomalo, a data quality platform aiming to automate issue detection with zero setup

Interview

Introduction How did you get involved in the area of data management? Can you describe what Anomalo is and the story behind it? Managing data quality is ostensibly about building trust in your data. What are the promises that data teams are able to make about the information in their control when they are using Anomalo?

What are some of the claims that cannot be made unequivocally when relying on data quality monitoring systems?

types of data quality issues identified

utility of automated vs programmatic tests

Can you describe how the Anomalo system is designed and implemented?

How have the design and goals of the platform changed or evolved since you started working on it?

What is your approach for validating changes to the business logic in your platform given the unpredictable nature of the system under test? model training/customization process statistical model seasonality/windowing CI/CD With any monitoring system the most challenging thing to do i

Summary Reverse ETL is a product category that evolved from the landscape of customer data platforms with a number of companies offering their own implementation of it. While struggling with the work of automating data integration workflows with marketing, sales, and support tools Brian Leonard accidentally discovered this need himself and turned it into the open source framework Grouparoo. In this episode he explains why he decided to turn these efforts into an open core business, how the platform is implemented, and the benefits of having an open source contender in the landscape of operational analytics products.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Brian Leonard about Grouparoo, an open source framework for managing your reverse ETL pipelines

Interview

Introduction How did you get involved in the area of data management? Can you describe what Grouparoo is and the story behind it? What are the core requirements for building a reverse ETL system?

What are the additional capabilities that users of the system ask for as they get more advanced in their usage?

Who is your target user for Grouparoo and how does that influence your priorities on feature development and UX design? What are the benefits of building an open source core for a reverse ETL platform as compared to the other commercial options? Can you describe the architecture and implementation of the Grouparoo project?

What are the additional systems that you have built to support the hosted offering? How have the design and goals of the

Summary Communication and shared context are the hardest part of any data system. In recent years the focus has been on data catalogs as the means for documenting data assets, but those introduce a secondary system of record in order to find the necessary information. In this episode Emily Riederer shares her work to create a controlled vocabulary for managing the semantic elements of the data managed by her team and encoding it in the schema definitions in her data warehouse. She also explains how she created the dbtplyr package to simplify the work of creating and enforcing your own controlled vocabularies.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Emily Riederer about defining and enforcing column contracts and controlled vocabularies for your data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by discussing some of the anti-patterns that you have encountered in data warehouse naming conventions and how it relates to the modeling approach? (e.g. star/snowflake schema, data vault, etc.) What are some of the types of contracts that can, and should, be defined and enforced in data workflows?

What are the boundaries where we should think about establishing those contracts?

What is the utility of column and table names for defining and enforcing contracts in analytical work? What is the process for establishing contractual elements in a naming schema?

Who should be involved in that design process? Who are the participants in the communication paths for column naming contracts?

What are some examples of context and details that can’t be captured in column names?

What are some options for managing that additional information and linking it to the naming cont

Summary This has been an active year for the data ecosystem, with a number of new product categories and substantial growth in existing areas. In an attempt to capture the zeitgeist Maura Church, David Wallace, Benn Stancil, and Gleb Mezhanskiy join the show to reflect on the past year and share their thought son the year to come.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. The first 10 people to request a personalized product tour will receive an exclusive Monte Carlo Swag box. Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Your host is Tobias Macey and today I’m interviewing Maura Church, David Wallace, Benn Stancil, and Gleb Mezhanskiy about the key themes of 2021 in the data ecosystem and what to expect for next year

Interview

Introduction

How did you get involved in the area of data management?

What were the main themes that you saw data practitioners and vendors focused on this year?

What is the major bottleneck for Data teams in 2021? Will it be the same in 2022? One of the ways to reason about progress in any domain is to look at what was the primary bottleneck of further progress (data adoption for decision making) at different points in time. In the data domain, we have seen a number of bottlenecks, for example, scaling data platforms, the answer to which was Hadoop and on-prem columnar stores and then cloud data warehouses such as Snowflake & BigQuery. Then the problem was data integration and transformation which was solved by data integration vendors and frameworks such as Fivetran / Airbyte, modern orchestration frameworks such as Dagster & dbt and “reverse-ETL” Hightouch. What is the main challenge now?

Will SQL be challenged as a primary interface to analytical data? In 2020 we’ve seen a few launches of post-SQL languages such as Malloy, Preql, metric layer query languages from Transform and Supergrain.

To what extent does speed matter? Over the past

Summary The data mesh is a thesis that was presented to address the technical and organizational challenges that businesses face in managing their analytical workflows at scale. Zhamak Dehghani introduced the concepts behind this architectural patterns in 2019, and since then it has been gaining popularity with many companies adopting some version of it in their systems. In this episode Zhamak re-joins the show to discuss the real world benefits that have been seen, the lessons that she has learned while working with her clients and the community, and her vision for the future of the data mesh.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m welcoming back Zhamak Dehghani to talk about her work on the data mesh book and the lessons learned over the past 2 years

Interview

Introduction How did you get involved in the area of data management? Can you start by giving a brief recap of the principles of the data mesh and the story behind it? How has your view of the principles of the data mesh changed since our conversation in July of 2019? What are some of the ways that your work on the data mesh book influenced your thinking on the practical elements of implementing a data mesh? What do you view as the as-yet-unknown elements of the technical and social design constructs that are needed for a sustainable data mesh implementation? In the opening of your book you state that "Data Mesh is a new approach in sourcing, managing, and accessing data for analytical use cases at scale". As with everything, scale is subjective, but what are some of the heuristics that you rely on for determining when a data mesh is an appropriate solution? What are some of the ways that data mesh concepts manifest at the boundaries of organizations? While the idea of federated access to data product quanta reduces the amount of coordination necessary at the organizational level, it raises the spectre of more complex logic required for consumers of multiple quanta. How can data mesh implementations mitigate the impact of this problem? What are some of the technical components that you have found to be best suited to the implementation of data elements within a mesh? What are the technological components that are still missing for a mesh-native data platform? How should an organization that wishes to implement a mesh style architecture think about the roles and skills that they will need on staff?

How can vendors factor into the solution?

What is the role of application developers in a data mesh ecosystem and how do they need to change their thinking around the interfaces that they provide in their products? What are the most interesting, innovative, or unexpected ways that you have seen data mesh principles used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data mesh implementations? When is a data mesh the wrong approach? What do you think the future of the data mesh will look like?

Contact Info

LinkedIn @zhamakd on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Data Engineering Podcast Data Mesh Interview Data Mesh Book Thoughtworks Expert Systems OpenLineage

Podcast Episode

Data Mesh Learning

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary One of the perennial challenges of data analytics is having a consistent set of definitions, along with a flexible and performant API endpoint for querying them. In this episode Artom Keydunov and Pavel Tiunov share their work on Cube.js and the various ways that it is being used in the open source community.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m interviewing Artyom Keydunov and Pavel Tiunov about Cube.js a framework for building analytics APIs to power your applications and BI dashboards

Interview

Introduction How did you get involved in the area of data management? Can you describe what Cube is and the story behind it? What are the main use cases and platform architectures that you are focused on?

Who are the target personas that will be using and managing Cube.js?

The name comes from the concept of an OLAP cube. Can you discuss the applications of OLAP cubes and their role in the current state of the data ecosystem?

How does the idea of an OLAP cube compare to the recent focus on a dedicated metrics layer?

What are the pieces of a data platform that might be replaced by Cube.js? Can you describe the design and architecture of the Cube platform?

How has the focus and target use case for the Cube platform evolved since you first started working on it?

One of the perpetually hard problems in computer science is cache management. How have you approached that challenge in the pre-aggregation layer of the Cube framework? What is your overarching design philosophy for the API of the Cube system? Can you talk through the workflow of someone building a cube and querying it from a downstream system?

What do the iteration cycles look like as you go from initial proof of concept to a more sophisticated usage of Cube.js

Summary Spark is a powerful and battle tested framework for building highly scalable data pipelines. Because of its proven ability to handle large volumes of data Capital One has invested in it for their business needs. In this episode Gokul Prabagaren shares his use for it in calculating your rewards points, including the auditing requirements and how he designed his pipeline to maintain all of the necessary information through a pattern of data enrichment.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m interviewing Gokul Prabagaren about how he is using Spark for real-world workflows at Capital One

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the types of data and workflows that you are responsible for at Capital one?

In terms of the three "V"s (Volume, Variety, Velocity), what is the magnitude of the data that you are working with?

What are some of the business and regulatory requirements that have to be factored into the solutions that you design? Who are the consumers of the data assets that you are producing? Can you describe the technical elements of the platform that you use for managing your data pipelines? What are the various ways that you are using Spark at Capital One? You wrote a post and presented at the Databricks conference about your experience moving from a data filtering to a data enrichment pattern for segmenting transactions. Can you give some context as to the use case and what your design process was for the initial implementation?

What were the shortcomings to that approach/business requirements which led you to refactoring the approach to one that maintained all of the data through the different processing stages?

What are some of t

Summary A/B testing and experimentation are the most reliable way to determine whether a change to your product will have the desired effect on your business. Unfortunately, being able to design, deploy, and validate experiments is a complex process that requires a mix of technical capacity and organizational involvement which is hard to come by. Chetan Sharma founded Eppo to provide a system that organizations of every scale can use to reduce the burden of managing experiments so that you can focus on improving your business. In this episode he digs into the technical, statistical, and design requirements for running effective experiments and how he has architected the Eppo platform to make the process more accessible to business and data professionals.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m interviewing Chetan Sharma about Eppo, a platform for building A/B experiments that are easier to manage

Interview

Introduction How did you get involved in the area of data management? Can you describe what Eppo is and the story behind it? What are some examples of the kinds of experiments that teams and organizations might want to conduct? What are the points of friction that What are the steps involved in designing, deploying, and analyzing the outcomes of an A/B experiment?

What are some of the statistical errors that are common when conducting an experiment?

What are the design and UX principles that you have focused on in Eppo to improve the workflow of building and analyzing experiments? Can you describe the system design of the Eppo platform?

What are the services or capabilities external to Eppo that are required for it to be effective? What are the integration points for adding Eppo to an organization’s existing platform?

B

Summary The modern data stack has been gaining a lot of attention recently with a rapidly growing set of managed services for different stages of the data lifecycle. With all of the available options it is possible to run a scalable, production grade data platform with a small team, but there are still sharp edges and integration challenges to work through. Peter Fishman and Dan Silberman experienced these difficulties firsthand and created Mozart Data to provide a single, easy to use option for getting started with the modern data stack. In this episode they explain how they designed a user experience to make working with data more accessibly by organizations without a data team, while allowing for more advanced users to build out more complex workflows. They also share their thoughts on the modern data ecosystem and how it improves the availability of analytics for companies of all sizes.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m interviewing Peter Fishman and Dan Silberman about Mozart Data and how they are building a unified experience for the modern data stack

Interview

Introduction How did you get involved in the area of data management? Can you describe what Mozart Data is and the story behind it? The promise of the "modern data stack" is that it’s all delivered as a service to make it easier to set up. What are the missing pieces that make something like Mozart necessary? What are the main workflows or industries that you are focusing on? Who are the main personas that you are building Mozart for?

How has that combination of user persona and industry focus informed your decisions around feature priorities and user experience?

Can you describe how you have architected the Mozart platform?

How have you approached the bu

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. With the improvements in streaming engines it is now possible to perform all of your data integration in near real time, but it can be challenging to understand the proper processing patterns to make that performant. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.

Announcements

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 our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m interviewing Ori Rafael about strategies for building stream and batch processing patterns for data lake analytics

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the state of the market for data lakes today?

What are the prevailing architectural and technological patterns that are being used to manage these systems?

Batch and streaming systems have been used in various combinations since the early days of Hadoop. The Lambda architecture has largely been abandoned, so what is the answer for today’s data lakes? What are the challenges presented by streaming approaches to data transformations?

The batch model for processing is intuitive despite its latency problems. What are the benefits that it provides?

The core concept for data orchestration is the DAG. How does that manifest in a streaming context? In batch processing idempotent/immutable datasets are created by re-running the entire pipeline when logic changes need to be made. Given that there is no definitive start or end of a stream, what are the options for amending logical errors in transformations? What are some of the da