Summary In this crossover episode, Max Beauchemin explores how multiplayer, multi‑agent engineering is transforming the way individuals and teams build data and AI systems. He digs into the shifting boundary between data and AI engineering, the rise of “context as code,” and how just‑in‑time retrieval via MCP and CLIs lets agents gather what they need without bloating context windows. Max shares hard‑won practices from going “AI‑first” for most tasks, where humans focus on orchestration and taste, and the new bottlenecks that appear — code review, QA, async coordination — when execution accelerates 2–10x. He also dives deep into Agor, his open‑source agent orchestration platform: a spatial, multiplayer workspace that manages Git worktrees and live dev environments, templatizes prompts by workflow zones, supports session forking and sub‑sessions, and exposes an internal MCP so agents can schedule, monitor, and even coordinate other agents.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Maxime Beauchemin about the impact of multi-player multi-agent engineering on individual and team velocity for building better data systemsInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of the types of work that you are relying on AI development agents for?As you bring agents into the mix for software engineering, what are the bottlenecks that start to show up?In my own experience there are a finite number of agents that I can manage in parallel. How does Agor help to increase that limit?How does making multi-agent management a multi-player experience change the dynamics of how you apply agentic engineering workflows?Contact Info LinkedInLinks AgorApache AirflowApache SupersetPresetClaude CodeCodexPlaywright MCPTmuxGit WorktreesOpencode.aiGitHub CodespacesOnaThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary

Data lakehouse architectures are gaining popularity due to the flexibility and cost effectiveness that they offer. The link that bridges the gap between data lake and warehouse capabilities is the catalog. The primary purpose of the catalog is to inform the query engine of what data exists and where, but the Nessie project aims to go beyond that simple utility. In this episode Alex Merced explains how the branching and merging functionality in Nessie allows you to use the same versioning semantics for your data lakehouse that you are used to from Git.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council and use code dataengpod20 to register today! Your host is Tobias Macey and today I'm interviewing Alex Merced, developer advocate at Dremio and co-author of the upcoming book from O'reilly, "Apache Iceberg, The definitive Guide", about Nessie, a git-like versioned catalog for data lakes using Apache Iceberg

Interview

Introduction How did you get involved in the area of data management? Can you describe what Nessie is and the story behind it? What are the core problems/complexities that Nessie is designed to solve? The closest analogue to Nessie that I've seen in the ecosystem is LakeFS. What are the features that would lead someone to choose one or the other for a given use case? Why would someone choose Nessie over native table-level branching in the Apache Iceberg spec? How do the versioning capabilities compare to/augment the data versioning in Iceberg? What are some of the sources of, and challenges in resolving, merge conflicts between table branches? Can you describe the architecture of Nessie? How have the design and goals of the project changed since it was first created? What is involved

Summary

The rapid growth of machine learning, especially large language models, have led to a commensurate growth in the need to store and compare vectors. In this episode Louis Brandy discusses the applications for vector search capabilities both in and outside of AI, as well as the challenges of maintaining real-time indexes of vector data.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! If you’re a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex’s magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It’s like having an analytics co-pilot built right into where you’re already doing your work. Then, when you’re ready to share, you can use Hex’s drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial of the Hex Team plan! Your host is Tobias Macey and today I'm interviewing Louis Brandy about building vector indexes in real-time for analytics and AI applications

Interview

Introduction How did you get involved in the area of data management? Can you describe what vector search is and how it differs from other search technologies?

What are the technical challenges related to providing vector search? What are the applications for vector search that merit the added complexity?

Vector databases have been gaining a lot of attention recently with the proliferation of LLM applicati

Summary

A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! If you’re a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex’s magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It’s like having an analytics co-pilot built right into where you’re already doing your work. Then, when you’re ready to share, you can use Hex’s drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex to get a 30-day free trial of the Hex Team plan! Your host is Tobias Macey and today I'm interviewing Brian Platz about using JSON-LD for building linked-data products

Interview

Introduction How did you get involved in the area of data management? Can you describe what the term "linked data product" means and some examples of when you might build one?

What is the overlap between knowledge graphs and "linked data products"?

What is JSON-LD?

What are the domains in which it is typically used? How does it assist in developing linked data products?

what are the characterist

Summary Agile methodologies have been adopted by a majority of teams for building software applications. Applying those same practices to data can prove challenging due to the number of systems that need to be included to implement a complete feature. In this episode Shane Gibson shares practical advice and insights from his years of experience as a consultant and engineer working in data about how to adopt agile principles in your data work so that you can move faster and provide more value to the business, while building systems that are maintainable and adaptable.

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 new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support. Your host is Tobias Macey and today I’m interviewing Shane Gibson about how to bring Agile practices to your data management workflows

Interview

Introduction How did you get involved in the area of data management? Can you describe what AgileData is and the story behind it? What are the main industries and/or use cases that you are focused on supporting? The data ecosystem has been trying on different paradigms from software development for some time now (e.g. DataOps, version control, etc.). What are the aspects of Agile that do and don’t map well to data engineering/analysis? One of the perennial challenges of data analysis is how to approach data modeling. How do you balance the need to provide value with the long-term impacts of incomplete or underinformed modeling decisions made in haste at the beginning of a project?

How do you design in affordances for refactoring of the data models without breaking downstream assets?

Another aspect of implementing data products/platforms is how to manage permissions and governance. What are the incremental ways that those principles can be incorporated early and evolved along with the overall analytical products? What are some of the organizational design strategies that you find most helpful when establishing or training a team who is working on data products? In order to have a useful target to work toward it’s necessary to understand what the data consumers are hoping to achieve. What are some of the challenges of doing requirements gathering for data products? (e.g. not knowing what information is available, consumers not understanding what’s hard vs. easy, etc.)

How do you work with the "customers" to help them understand what a reasonable scope is and translate that to the actual project stages for the engineers?

What are some of the perennial questions or points of confusion that you have had to address with your clients on how to design and implement analytical assets? What are the most interesting, innovative, or unexpected ways that you have seen agile principles used for data? What are the most interesting, unexpected, or challenging lessons that you have learned while working on AgileData? When is agile the wrong choice for a data project? What do you have planned for the future of AgileData?

Contact Info

LinkedIn @shagility on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

AgileData OptimalBI How To Make Toast Data Mesh Information Product Canvas DataKitchen

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Great Expectations

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Soda Data

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Google DataStore Unfix.work Activity Schema

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Data Vault

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Star Schema Lean Methodology Scrum Kanban

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

Sponsored By: Atlan:

Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?

Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. 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 and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.Prefect:

Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit…

Summary Metadata is the lifeblood of your data platform, providing information about what is happening in your systems. A variety of platforms have been developed to capture and analyze that information to great effect, but they are inherently limited in their utility due to their nature as storage systems. In order to level up their value a new trend of active metadata is being implemented, allowing use cases like keeping BI reports up to date, auto-scaling your warehouses, and automated data governance. In this episode Prukalpa Sankar joins the show to talk about the work she and her team at Atlan are doing to push this capability into the mainstream.

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 new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! 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. Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. 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 Prukalpa Sankar about how data platforms can benefit from the idea of "active metadata" and the work that she and her team at Atlan are doing to make it a reality

Interview

Introduction How did you get involved in the area of data management? Can you describe what "active metadata" is and how it differs from the current approaches to metadata systems? What are some of the use cases that "active metadata" can enable for data producers and consumers?

What are the points of friction that those users encounter in the current formulation of metadata systems?

Central metadata systems/data catalogs came about as a solution to the challenge of integrating every data tool with every other data tool, giving a single place to integrate. What are the lessons that are being learned from the "modern data stack" that can be applied to centralized metadata? Can you describe the approach that you are taking at Atlan to enable the adoption of "active metadata"?

What are the architectural capabilities that you had to build to power the outbound traffic flows?

How are you addressing the N x M integration problem for pushing metadata into the necessary contexts at Atlan?

What are the interfaces that are necessary for receiving systems to be able to make use of the metadata that is being delivered? How does the type/category of metadata impact the type of integration that is necessary?

What are some of the automation possibilities that metadata activation offers for data teams?

What are the cases where you still need a human in the loop?

What are the most interesting, innovative, or unexpected ways that you have seen active metadata capabilities used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on activating metadata for your users? When is an active approach to metadata the wrong choice? What do you have planned for the future of Atlan and active metadata?

Contact Info

LinkedIn @prukalpa on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Atlan What is Active Metadata? Segment

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Zapier ArgoCD Kubernetes Wix AWS Lambda Modern Data Culture Blog Post

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

Support Data Engineering Podcast

Summary The modern data stack is a constantly moving target which makes it difficult to adopt without prior experience. In order to accelerate the time to deliver useful insights at organizations of all sizes that are looking to take advantage of these new and evolving architectures Tarush Aggarwal founded 5X Data. In this episode he explains how he works with these companies to deploy the technology stack and pairs them with an experienced engineer who assists with the implementation and training to let them realize the benefits of this architecture. He also shares his thoughts on the current state of the ecosystem for modern data vendors and trends to watch as we move into the future.

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! 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. So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. Your host is Tobias Macey and today I’m interviewing Tarush Agarwal about how he and his team are helping organizations streamline adoption of the modern data stack

Interview

Introduction How did you get involved in the area of data management? Can you describe what you are doing at 5x and the story behind it? How has your focus and operating model shifted since we spoke a year ago?

What are the biggest shifts in the market for data management that you have seen in that time?

What are the main challenges that your customers are facing when they start working with you? What are the components that you are relying on to build repeatable data platforms for your customers?

What are the sharp edges that you have had to smooth out to scale your implementation of those

Summary Databases are an important component of application architectures, but they are often difficult to work with. HarperDB was created with the core goal of being a developer friendly database engine. In the process they ended up creating a scalable distributed engine that works across edge and datacenter environments to support a variety of novel use cases. In this episode co-founder and CEO Stephen Goldberg shares the history of the project, how it is architected to achieve their goals, and how you can start using it 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! 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. So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. 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 Stephen Goldberg about HarperDB, a developer-friendly distributed database engine designed to scale acros

Summary There are a wealth of options for managing structured and textual data, but unstructured binary data assets are not as well supported across the ecosystem. As organizations start to adopt cloud technologies they need a way to manage the distribution, discovery, and collaboration of data across their operating environments. To help solve this complicated challenge Krishna Subramanian and her co-founders at Komprise built a system that allows you to treat use and secure your data wherever it lives, and track copies across environments without requiring manual intervention. In this episode she explains the difficulties that everyone faces as they scale beyond a single operating environment, and how the Komprise platform reduces the burden of managing large and heterogeneous collections of unstructured files.

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! 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. So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. Your host is Tobias Macey and today I’m interviewing Krishna Subramanian about her work at Komprise to generate value from unstructured file and object data across storage formats and locations

Interview

Introduction How did you get involved in the area of data management? Can you describe what Komprise is and the story behind it? Who are the target customers of the Komprise platform?

What are the core use cases that you are focused on supporting?

How would you characterize the common approaches to managing file storage solutions for hybrid cloud environments?

What are some of the shortcomings of the enterprise storage providers’ met

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 Collecting, integrating, and activating data are all challenging activities. When that data pertains to your customers it can become even more complex. To simplify the work of managing the full flow of your customer data and keep you in full control the team at Rudderstack created their eponymous open source platform that allows you to work with first and third party data, as well as build and manage reverse ETL workflows. In this episode CEO and founder Soumyadeb Mitra explains how Rudderstack compares to the various other tools and platforms that share some overlap, how to set it up for your own data needs, and how it is architected to scale to meet demand.

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! 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. 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 Soumyadeb Mitra about his experience as the founder of Rudderstack and its role in your data platform

Interview

Introduction How did you get involved in the area of data management? Can you describe what Rudderstack is and the story behind it? What are the main use cases that Rudderstack is designed to support? Who are the target users of Rudderstack?

How does the availability of the managed cloud service change the user profiles that you can target? How do these user profiles influence your focus and prioritization of features and user experience?

How would you characterize the position of Rudderstack in the current data ecosystem?

What other tools/systems might you replace with Rudderstack?

How do you think about the application of Rudderstack compared to tools for data integration (e.g. Singer, Stitch, Fivetran) and reverse ETL (e.g. Grouparoo, Hightouch, Census)? Can you describe how the Rudderstack platform is desig

Summary There are many dimensions to the work of protecting the privacy of users in our data. When you need to share a data set with other teams, departments, or businesses then it is of utmost importance that you eliminate or obfuscate personal information. In this episode Will Thompson explores the many ways that sensitive data can be leaked, re-identified, or otherwise be at risk, as well as the different strategies that can be employed to mitigate those attack vectors. He also explains how he and his team at Privacy Dynamics are working to make those strategies more accessible to organizations so that you can focus on all of the other tasks required of you.

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! 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. 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 Will Thompson about managing data privacy concerns for data sets used in analytics and machine learning

Interview

Introduction How did you get involved in the area of data management? Data privacy is a multi-faceted problem domain. Can you start by enumerating the different categories of privacy concern that are involved in analytical use cases? Can you describe what Privacy Dynamics is and the story behind it?

Which categor(y|ies) are you focused on addressing?

What are some of the best practices in the definition, protection, and enforcement of data privacy policies?

Is there a data security/privacy equivalent to the OWASP top 10?

What are some of the techniques that are available for anonymizing data while maintaining statistical utility/significance?

What are some of the engineering/systems capabilities that are required for data (platform) engineers to incorporate these practices in their platforms?

What are the tradeoffs of encryption vs. obfuscation when anonymizing data? What are some of the types of PII that are non-obvious? What are the risks associated with data re-identification, and what are some of the vectors that might be exploited to achieve that?

How can privacy risks mitigation be maintained as new data sources are introduced that might contribute to these re-identification vectors?

Can you describe how Privacy Dynamics is implemented?

What are the most challenging engineering problems that you are dealing with?

How do you approach validation of a data set’s privacy? What have you found to be useful heuristics for identifying private data?

What are the risks of false positives vs. false negatives?

Can you describe what is involved in integrating the Privacy Dynamics system into an existing data platform/warehouse?

What would be required to integrate with systems such as Presto, Clickhouse, Druid, etc.?

What are the most interesting, innovative, or unexpected ways that you have seen Privacy Dynamics used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Privacy Dynamics? When is Privacy Dynamics the wrong choice? What do you have planned for the future of Privacy Dynamics?

Contact Info

LinkedIn @willseth on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Privacy Dynamics Pandas

Podcast Episode – Pandas For Data Engineering

Homomorphic Encryption Differential Privacy Immuta

Podcast Episode

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

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Summary Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications. Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. He recently wrote a book on effective patterns for Pandas code, and in this episode he shares advice on how to write efficient data processing routines that will scale with your data volumes, while being understandable and maintainable.

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! 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. 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 Matt Harrison about useful tips for using Pandas for data engineering projects

Interview

Introduction How did you get involved in the area of data management? What are the main tasks that you have seen Pandas used for in a data engineering context? What are some of the common mistakes that can lead to poor performance when scaling to large data sets? What are some of the utility features that you have found most helpful for data processing? One of the interesting add-ons to Pandas is its integration with Arrow. What are some of the considerations for how and when to use the Arrow capabilities vs. out-of-the-box Pandas? Pandas is a tool that spans data processing and data science. What are some of the ways that data engineers should think about writing their code to make it accessible to data scientists for supporting collaboration across data workflows? Pandas is often used for transformation logic. What are some of the ways that engineers should approach the design of their code to make it understandable and maint

Summary Data engineering is a relatively young and rapidly expanding field, with practitioners having a wide array of experiences as they navigate their careers. Ashish Mrig currently leads the data analytics platform for Wayfair, as well as running a local data engineering meetup. In this episode he shares his career journey, the challenges related to management of data professionals, and the platform design that he and his team have built to power analytics at a large company. He also provides some excellent insights into the factors that play into the build vs. buy decision at different organizational 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! 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. 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 Ashish Mrig about his path as a data engineer

Interview

Introduction How did you get involved in the area of data management? You currently lead a data engineering team at a relatively large company. What are the topics that account for the majority of your time and energy? What are some of the most valuable lessons that you’ve learned about managing and motivating teams of data professionals? What has been your most consistent challenge across the different generations of the data ecosystem? How is your current data platform architected? Given the current state of the technology and services landscape, how would you approach the design and implementation of a greenfield rebuild of your platform? What are some of the pitfalls that you have seen data teams encounter most frequently? You are running a data engineering meetup for your local community in the Boston area. What have been some of the recurring themes that are discussed in those events?

Contact Info

Medium Blog LinkedIn

Summary Applications of data have grown well beyond the venerable business intelligence dashboards that organizations have relied on for decades. Now it is being used to power consumer facing services, influence organizational behaviors, and build sophisticated machine learning systems. Given this increased level of importance it has become necessary for everyone in the business to treat data as a product in the same way that software applications have driven the early 2000s. In this episode Brian McMillan shares his work on the book "Building Data Products" and how he is working to educate business users and data professionals about the combination of technical, economical, and business considerations that need to be blended for these projects to succeed.

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! 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. 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 Brian McMillan about building data products and his book to introduce the work of data analysts and engineers to non-programmers

Interview

Introduction How did you get involved in the area of data management? Can you describe what motivated you to write a book about the work of building data products?

Who is your target audience? What are the main goals that you are trying to achieve through the book?

What

Summary Data observability is a set of technical and organizational capabilities related to understanding how your data is being processed and used so that you can proactively identify and fix errors in your workflows. In this episode Metaplane founder Kevin Hu shares his working definition of the term and explains the work that he and his team are doing to cut down on the time to adoption for this new set of practices. He discusses the factors that influenced his decision to start with the data warehouse, the potential shortcomings of that approach, and where he plans to go from there. This is a great exploration of what it means to treat your data platform as a living system and apply state of the art engineering to it.

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! 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. 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 Kevin Hu about Metaplane, a platform aiming to provide observability for modern data stacks, from warehouses to BI dashboards and everything in between.

Interview

Introduction How did you get involved in the area of data management? Can you describe what Metaplane is and the story behind it? Data observability is an area that has seen a huge amount of activity over the past couple of years. What is your working definition of that term?

What are the areas of differentiation that you see across vendors in the space?

Can you describe how the Metaplane platform is architected?

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

establishing seasonality in data metrics blind spots from operating at the level of the data warehouse What are the most interesting, innovative, or unexpected ways that you have seen Metaplane used? What are the most interesti

Summary There is a wealth of tools and systems available for processing data, but the user experience of integrating them and building workflows is still lacking. This is particularly important in large and complex organizations where domain knowledge and context is paramount and there may not be access to engineers for codifying that expertise. Raj Bains founded Prophecy to address this need by creating a UI first platform for building and executing data engineering workflows that orchestrates Airflow and Spark. Rather than locking your business logic into a proprietary storage layer and only exposing it through a drag-and-drop editor Prophecy synchronizes all of your jobs with source control, allowing an easy bi-directional interaction between code first and no-code experiences. In this episode he shares his motivations for creating Prophecy, how he is leveraging the magic of compilers to translate between UI and code oriented representations of logic, and the organizational benefits of having a cohesive experience designed to bring business users and domain experts into the same platform as data engineers and analysts.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy! 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! 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. 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 Raj Bains about Prophecy, a low-code data engineering platform built on Spark and Airflow

Interview

Introduction How did you get involved in the area of data management? Can you describe what you are building at Prophecy and the story behind it? There are a huge number of too

Summary Data lakes are gaining popularity due to their flexibility and reduced cost of storage. Along with the benefits there are some additional complexities to consider, including how to safely integrate new data sources or test out changes to existing pipelines. In order to address these challenges the team at Treeverse created LakeFS to introduce version control capabilities to your storage layer. In this episode Einat Orr and Oz Katz explain how they implemented branching and merging capabilities for object storage, best practices for how to use versioning primitives to introduce changes to your data lake, how LakeFS is architected, and how you can start using it for your own data platform.

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 $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help you derive real results from your data at dataengineeringpodcast.com/immuta. Today’s episode of the Data Engineering Podcast is sponsored by Datadog, a SaaS-based monitoring and analytics platform for cloud-scale infrastructure, applications, logs, and more. Datadog uses machine-learning based algorithms to detect errors and anomalies across your entire stack—which reduces the time it takes to detect and address outages and helps promote collaboration between Data Engineering, Operations, and the rest of the company. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial. If you start a trial and install Datadog’s agent, Datadog will send you a free T-shirt. Your host is Tobias Macey and today I’m interviewing Einat Orr and Oz Katz about their work at Treeverse on the LakeFS system for versioning your data lakes the same way you version your code.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what LakeFS is and why you built it?

There are a number of tools and platforms that support data virtualization and data versioning. How does LakeFS compare to the available options? (e.g. Alluxio, Denodo, Pachyderm, DVC, etc.)

What are the primary use cases that LakeFS enables? For someone who wants to use LakeFS what is involved in getting it set up? How is LakeFS implemented?

How has the design of the system changed or evolved since you began working on it? What assumptions did you have going into it which have since been invalidated or modified?

How does the workflow for an engineer or analyst change from working directly against S3 to running against the LakeFS interface? How do you handle merge conflicts and resolution?

What

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools. At WeWork they needed a system that would provide visibility into their Airflow pipelines and the outputs produced. In this episode Julien Le Dem and Willy Lulciuc explain how they built Marquez to serve that need, how it is architected, and how it compares to other options that you might be considering. Even if you already have a metadata repository this is worth a listen to learn more about the value that visibility of your data can bring to your organization.

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 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. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse? Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics. Their web based transformation tool with built in collaboration features lets your analysts own the full lifecycle of data in your warehouse. Featuring built in version control integration, real-time error checking for their SQL code, data quality tests, scheduling, and a data catalog with annotation capabilities it’s everything you need to keep your data warehouse in order. Sign up for a free trial today at dataengineeringpodcast.com/dataform and email [email protected] with the subject "Data Engineering Podcast" to get a hands-on demo from one of their data experts. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference, the Strata Data conference, and PyCon US. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Willy Lulciuc and Julien Le Dem about Marquez, an open source platform to collect, aggregate, and visualize a data ecosystem’s metadata

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Marquez is?

What was missing in existing metadata management platforms that necessitated the creation of Marquez?

How do the capabilities of Marquez compare with tools and services that bill themselves as data catalogs?

How does it compare to the Amundsen platform that Lyft recently released?

What are some of the tools or platforms that are currently integrated with Marquez and what additional integrations would you like to see? What are some of the capabilities that are unique to Marquez and how are you using them at WeWork? What are the primary resource types that you support in Marquez?

What are some of the lowest common denominator attributes that are necessary and useful to track in a metadata repository?

Can you explain how Marquez is architected and how the design has evolved since you first began working on it?

Many metadata management systems are simply a service layer on top of a separate data storage engine. What are the benefits of using PostgreSQL as the system of record for Marquez?

What are some of the complexities that arise from relying on a relational engine as opposed to a document store or graph database?

How is the metadata itself stored and managed in Marquez?

How much up-front data modeling is necessary and what types of schema representations are supported?

Can you talk through the overall workflow of someone using Marquez in their environment?

What is involved in registering and updating datasets? How do you define and track the health of a given dataset? What are some of the interesting questions that can be answered from the information stored in Marquez?

What were your assumptions going into this project and how have they been challenged or updated as you began using it for production use cases? For someone who is interested in using Marquez what is involved in deploying and maintaining an installation of it? What have you found to be the most challenging or unanticipated aspects of building and maintaining a metadata repository and data discovery platform? When is Marquez the wrong choice for a metadata repository? What do you have planned for the future of Marquez?

Contact Info

Julien Le Dem

@J_ on Twitter Email julienledem on GitHub

Willy

LinkedIn @wslulciuc on Twitter wslulciuc on GitHub

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Marquez

DataEngConf Presentation

WeWork Canary Yahoo Dremio Hadoop Pig Parquet

Podcast Episode

Airflow Apache Atlas Amundsen

Podcast Episode

Uber DataBook LinkedIn DataHub Iceberg Table Format

Podcast Episode

Delta Lake

Podcast Episode

Great Expectations data pipeline unit testing framework

Podcast.init Episode

Redshift SnowflakeDB

Podcast Episode

Apache Kafka Schema Registry

Podcast Episode

Open Tracing Jaeger Zipkin DropWizard Java framework Marquez UI Cayley Graph Database Kubernetes Marquez Helm Chart Marquez Docker Container Dagster

Podcast Episode

Luigi DBT

Podcast Episode

Thrift Protocol Buffers

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"…

Summary In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can start using it today to create reliable and well-tested reports in your favorite data warehouse.

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 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. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Drew Banin about DBT, the Data Build Tool, a toolkit for building analytics the way that developers build applications

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what DBT is and your motivation for creating it? Where does it fit in the overall landscape of data tools and the lifecycle of data in an analytics pipeline? Can you talk through the workflow for someone using DBT? One of the useful features of DBT for stability of analytics is the ability to write and execute tests. Can you explain how those are implemented? The packaging capabilities are beneficial for enabling collaboration. Can you talk through how the packaging system is implemented?

Are these packages driven by Fishtown Analytics or the dbt community?

What are the limitations of modeling everything as a SELECT statement? Making SQL code reusable is notoriously difficult. How does the Jinja templating of DBT address this issue and what are the shortcomings?

What are your thoughts on higher level approaches to SQL that compile down to the specific statements?

Can you explain how DBT is implemented and how the design has evolved since you first began working on it? What are some of the features of DBT that are often overlooked which you find particularly useful? What are some of the most interesting/unexpected/innovative ways that you have seen DBT used? What are the additional features that the commercial version of DBT provides? What are some of the most useful or challenging lessons that you have learned in the process of building and maintaining DBT? When is it the wrong choice? What do you have planned for the future of DBT?

Contact Info

Email @drebanin on Twitter drebanin on GitHub

Parting Question

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

Links

DBT Fishtown Analytics 8Tracks Internet Radio Redshift Magento Stitch Data Fivetran Airflow Business Intelligence Jinja template language BigQuery Snowflake Version Control Git Continuous Integration Test Driven Development Snowplow Analytics

Podcast Episode

dbt-utils We Can Do Better Than SQL blog post from EdgeDB EdgeDB Looker LookML

Podcast Interview

Presto DB

Podcast Interview

Spark SQL Hive Azure SQL Data Warehouse Data Warehouse Data Lake Data Council Conference Slowly Changing Dimensions dbt Archival Mode Analytics Periscope BI dbt docs dbt repository

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

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