Summary Data engineering is a large and growing subject, with new technologies, specializations, and "best practices" emerging at an accelerating pace. This podcast does its best to explore this fractal ecosystem, and has been at it for the past 5+ years. In this episode Joe Reis, founder of Ternary Data and co-author of "Fundamentals of Data Engineering", turns the tables and interviews the host, Tobias Macey, about his journey into podcasting, how he runs the show behind the scenes, and the other things that occupy his time.

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. Your host is Tobias Macey and today we’re flipping the script. Joe Reis of Ternary Data will be interviewing me about my time as the host of this show and my perspectives on the data ecosystem

Interview

Introduction How did you get involved in the area of data management? Now I’ll hand it off to Joe…

Joe’s Notes

You do a lot of podcasts. Why? Podcast.init started in 2015, and your first episode of Data Engineering was published January 14, 2017. Walk us through the start of these podcasts. why not a data science podcast? why DE? You’ve published 306 of shows of the Data Engineering Podcast, plus 370 for the init podcast, then you’ve got a new ML podcast. How have you kept the motivation over the years? What’s the process for the show (finding guests, topics, etc….recording, publishing)? It’s a lot of work. Walk us through this process. You’ve done a ton of shows and have a lot of context with what’s going on in the field of both data engineering and Python. What have been some of the

Summary Building and maintaining reliable data assets is the prime directive for data engineers. While it is easy to say, it is endlessly complex to implement, requiring data professionals to be experts in a wide range of disparate topics while designing and implementing complex topologies of information workflows. In order to make this a tractable problem it is essential that engineers embrace automation at every opportunity. In this episode Chris Riccomini shares his experiences building and scaling data operations at WePay and LinkedIn, as well as the lessons he has learned working with other teams as they automated their own systems.

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. Your host is Tobias Macey and today I’m interviewing Chris Riccomini about building awareness of data usage into CI/CD pipelines for application development

Interview

Introduction How did you get involved in the area of data management? What are the pieces of data platforms and processing that have been most difficult to scale in an organizational sense? What are the opportunities for automation to alleviate some of the toil that data and analytics engineers get caught up in? The application delivery ecosystem has been going through ongoing transformation in the form of CI/CD, infrastructure as code, etc. What are the parallels in the data ecosystem that are still nascent? What are the principles that still need to be translated for data practitioners? Which are subject to impedance mismatch and may never make sense to translate? As someone with a software engineering background and extensive e

Summary The perennial challenge of data engineers is ensuring that information is integrated reliably. While it is straightforward to know whether a synchronization process succeeded, it is not always clear whether every record was copied correctly. In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. In this episode they explain how the utility is implemented to run quickly and how you can start using it in your own data workflows to ensure that your data warehouse isn’t missing any records from your source systems.

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! Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? Tonic.ai does exactly that. With Tonic, you can generate fake data that looks, acts, and behaves like production because it’s made from production. Using universal data connectors and a flexible API, Tonic integrates seamlessly into your existing pipelines and allows you to shape and size your data to the scale, realism, and degree of privacy that you need. The platform offers advanced subsetting, secure de-identification, and ML-driven data synthesis to create targeted test data for all of your pre-production environments. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. Shorten development cycles, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data, with Tonic.ai. Data Engineering Podcast listeners can sign up for a free 2-week sandbox account, go to dataengineeringpodcast.com/tonic today to give it a try! 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. 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

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Master Data Management (MDM) is the process of building consensus around what the information actually means in the context of the business and then shaping the data to match those semantics. In this episode Malcolm Hawker shares his years of experience working in this domain to explore the combination of technical and social skills that are necessary to make an MDM project successful both at the outset and over the long term.

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! Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? Tonic.ai does exactly that. With Tonic, you can generate fake data that looks, acts, and behaves like production because it’s made from production. Using universal data connectors and a flexible API, Tonic integrates seamlessly into your existing pipelines and allows you to shape and size your data to the scale, realism, and degree of privacy that you need. The platform offers advanced subsetting, secure de-identification, and ML-driven data synthesis to create targeted test data for all of your pre-production environments. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. Shorten development cycles, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data, with Tonic.ai. Data Engineering Podcast listeners can sign up for a free 2-week sandbox account, go to dataengineeringpodcast.com/tonic today to give it a try! 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

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

Podcast Episode

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 Unstructured data takes many forms in an organization. From a data engineering perspective that often means things like JSON files, audio or video recordings, images, etc. Another category of unstructured data that every business deals with is PDFs, Word documents, workstation backups, and countless other types of information. Aparavi was created to tame the sprawl of information across machines, datacenters, and clouds so that you can reduce the amount of duplicate data and save time and money on managing your data assets. In this episode Rod Christensen shares the story behind Aparavi and how you can use it to cut costs and gain value for the long tail of your unstructured data.

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! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl 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. Your host is Tobias Macey and today I’m interviewing Rod Christensen about Aparavi, a platform designed to find and unlock the value of data, no matter where it lives

Interview

Introduction How did you get involved in the area of data management? Can you describe what Aparavi is and the story behind it? Who are the target customers for Aparavi and how does that inform your product roadmap and messaging? What are some of th

Summary The best way to make sure that you don’t leak sensitive data is to never have it in the first place. The team at Skyflow decided that the second best way is to build a storage system dedicated to securely managing your sensitive information and making it easy to integrate with your applications and data systems. In this episode Sean Falconer explains the idea of a data privacy vault and how this new architectural element can drastically reduce the potential for making a mistake with how you manage regulated or personally identifiable information.

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 all of that information 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 you can take advantage of active metadata and escape the chaos. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. 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. Your host is Tobias Macey and today I’m interviewing Sean Falconer about the idea of a data privacy vault and how the Skyflow team are working to make it turn-key

Interview

Introduction How did you get involved in the area of data management? Can you describe what Skyflow is and the story behind it? What is a "data privacy vault" and how does it differ from strategies such as privacy engineering or existing data governance patterns? What are the primary use cases and capabilities that you are focused on solving for with Skyflow?

Who is the target customer for Skyflow (e.g. how does it enter an organization)?

How is the Skyflow platform architected?

How have the design and goals of the system changed or evolved over time?

Can you describe the process of integrating with Skyflow at the application level? For organizations that are building analytical capabilities on top of the data managed in their applications, what are the interactions with Skyflow at each of the stages in the data lifecycle? One of the perennial problems with distributed systems is the challenge of joining data across machine boundaries. How do you mitigate that problem? On your website there are different "vaults" advertised in the form of healthcare, fintech, and PII. What are the different requirements across each of those problem domains?

What are the commonalities?

As a relatively new company in an emerging product category, what are some of the customer education challenges that you are facing? What are the most interesting, innovative, or unexpected ways that you have seen Skyflow used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Skyflow? When is Skyflow the wrong choice? What do you have planned for the future of Skyflow?

Contact Info

LinkedIn @seanfalconer on Twitter Website

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other 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

Skyflow Privacy Engineering Data Governance Homomorphic Encryption Polymorphic Encryption

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

Support Data Engineering Podcast

Summary A large fraction of data engineering work involves moving data from one storage location to another in order to support different access and query patterns. Singlestore aims to cut down on the number of database engines that you need to run so that you can reduce the amount of copying that is required. By supporting fast, in-memory row-based queries and columnar on-disk representation, it lets your transactional and analytical workloads run in the same database. In this episode SVP of engineering Shireesh Thota describes the impact on your overall system architecture that Singlestore can have and the benefits of using a cloud-native database engine for your next application.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription 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. 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 becom

Summary There are very few tools which are equally useful for data engineers, data scientists, and machine learning engineers. WhyLogs is a powerful library for flexibly instrumenting all of your data systems to understand the entire lifecycle of your data from source to productionized model. In this episode Andy Dang explains why the project was created, how you can apply it to your existing data systems, and how it functions to provide detailed context for being able to gain insight into all of your data processes.

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! This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl 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. The most important piece of any data project is the data itself, which is why it is critical that your data source is high quality. PostHog is your all-in-one product analytics suite including product analysis, user funnels, feature flags, experimentation, and it’s open source so you can host it yourself or let them do it for you! You have full control over your data and their plugin system lets you integrate with all of your other data tools, including data warehouses and SaaS platforms. Give it a try today with their generous free tier at dataengineeringpodcast.com/posthog Your host is Tobias Macey and today I’m interviewing Andy Dang about powering observability of AI systems with the whylogs data logging library

Interview

Introduction How did you get involved in the area of data management? Can you describe what Whylabs is and the story behind it? How is "data logging" differentiated from logging for the purpose of debugging and observability of software logic? What are the use cases that you are aiming to support with Whylogs?

How does it compare to libraries and services like Great Expectations/Monte Carlo/Soda Data/Datafold etc.

Can you describe how Whylogs is implemented?

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

How do you maintain feature parity between the Python and Java integrations? How do you structure the log events and metadata to provide detail and context for data applications?

How does that structure support aggregation and interpretation/analysis of the log information?

What is the process for integrating Whylogs into an existing project?

Once you ha

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

Announcements

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

Interview

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

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

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

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

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

Summary Data integration is a critical piece of every data pipeline, yet it is still far from being a solved problem. There are a number of managed platforms available, but the list of options for an open source system that supports a large variety of sources and destinations is still embarrasingly short. The team at Airbyte is adding a new entry to that list with the goal of making robust and easy to use data integration more accessible to teams who want or need to maintain full control of their data. In this episode co-founders John Lafleur and Michel Tricot share the story of how and why they created Airbyte, discuss the project’s design and architecture, and explain their vision of what an open soure data integration platform should offer. If you are struggling to maintain your extract and load pipelines or spending time on integrating with a new system when you would prefer to be working on other projects then this is definitely a conversation worth listening to.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. 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. Your host is Tobias Macey and today I’m interviewing Michel Tricot and John Lafleur about Airbyte, an open source framework for building data integration pipelines.

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Airbyte is and the story behind it? Businesses and data engineers have a variety of options for how to manage their data integration. How would you characterize the overall landscape and how does Airbyte distinguish itself in that space? How would you characterize your target users?

How have those personas instructed the priorities and design of Airbyte? What do you see as the benefits and tradeoffs of a UI oriented data integration platform as compared to a code first approach?

what are the complex/challenging elements of data integration that makes it such a slippery problem? motivation for creating open source ELT as a business Can you describe how the Airbyte platform is implemented?

What was your motivation for choosing Java as the primary language?

incidental complexity of forcing all connectors to be packaged as containers shortcomings of the Singer specification/motivation for creating a backwards incompatible interface perceived potential for community adoption of Airbyte specification tradeoffs of using JSON as interchange format vs. e.g. protobuf/gRPC/Avro/etc.

information lost when converting records to JSON types/how to preserve that information (e.g. field constraints, valid enums, etc.)

interfaces/extension points for integrating with other tools, e.g. Dagster abstraction layers for simplifying implementation of new connectors tradeoffs of storing all connectors in a monorepo with the Airbyte core

impact of community adoption/contributions

What is involved in setting up an Airbyte installation? What are the available axes for scaling an Airbyte deployment? challenges of setting up and maintaining CI environment for Airbyte How are you managing governance and long term sustainability of the project? What are some of the most interesting, unexpected, or innovative ways that you have seen Airbyte used? What are the most interesting, unexpected, or challenging lessons that you have learned while building Airbyte? When is Airbyte the wrong choice? What do you have planned for the future of the project?

Contact Info

Michel

LinkedIn @MichelTricot on Twitter michel-tricot on GitHub

John

LinkedIn @JeanLafleur on Twitter johnlafleur on GitHub

Parting Question

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

Links

Airbyte Liveramp Fivetran

Podcast Episode

Stitch Data Matillion DataCoral

Podcast Episode

Singer Meltano

Podcast Episode

Airflow

Podcast.init Episode

Kotlin Docker Monorepo Airbyte Specification Great Expectations

Podcast Episode

Dagster

Data Engineering Podcast Episode Podcast.init Episode

Prefect

Podcast Episode

DBT

Podcast Episode

Kubernetes Snowflake

Podcast Episode

Redshift Presto Spark Parquet

Podcast Episode

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

Support Data Engineering Podcast

Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how their teams are organized to allow for rapid growth and massive scale. Getting an inside look at the companies behind the services we use is always useful, and this conversation was no exception.

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 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 in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. 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 Vadim Semenov about how data engineers work at DataDog

Interview

Introduction How did you get involved in the area of data management? For anyone who isn’t familiar with DataDog, can you start by describing the types and volumes of data that you’re dealing with? What are the main components of your platform for managing that information? How are the data teams at DataDog organized and what are your primary responsibilities in the organization? What are some of the complexities and challenges that you face in your work as a result of the volume of data that you are processing?

What are some of the strategies which have proven to be most useful in overcoming those challenges?

Who are the main consumers of your work and how do you build in feedback cycles to ensure that their needs are being met? Given that the majority of the data being ingested by DataDog is timeseries, what are your lifecycle and retention policies for that information? Most of the data that you are working with is customer generated from your deployed agents and API integrations. How do you manage cleanliness and schema enforcement for the events as they are being delivered? What are some of the upcoming projects that you have planned for the upcoming months and years? What are some of the technologies, patterns, or practices that you are hoping to adopt?

Contact Info

LinkedIn @databuryat 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 Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

DataDog Hadoop Hive Yarn Chef SRE == Site Reliability Engineer Application Performance Management (APM) Apache Kafka RocksDB Cassandra Apache Parquet data serialization format SLA == Service Level Agreement WatchDog Apache Spark

Podcast Episode

Apache Pig Databricks JVM == Java Virtual Machine Kubernetes SSIS (SQL Server Integration Services) Pentaho JasperSoft Apache Airflow

Podcast.init Episode

Apache NiFi

Podcast Episode

Luigi Dagster

Podcast Episode

Prefect

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

Support Data Engineering Podcast

Summary Transactional databases used in applications are optimized for fast reads and writes with relatively simple queries on a small number of records. Data warehouses are optimized for batched writes and complex analytical queries. Between those use cases there are varying levels of support for fast reads on quickly changing data. To address that need more completely the team at Materialize has created an engine that allows for building queryable views of your data as it is continually updated from the stream of changes being generated by your applications. In this episode Frank McSherry, chief scientist of Materialize, explains why it was created, what use cases it enables, and how it works to provide fast queries on continually updated data.

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 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 in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. 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 Frank McSherry about Materialize, an engine for maintaining materialized views on incrementally updated data from change data captures

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Materialize is and the problems that you are aiming to solve with it?

What was your motivation for creating it?

What use cases does Materialize enable?

What are some of the existing tools or systems that you have seen employed to address those needs which can be replaced by Materialize? How does it fit into the broader ecosystem of data tools and platforms?

What are some of the use cases that Materialize is uniquely able to support? How is Materialize architected and how has the design evolved since you first began working on it? Materialize is based on your timely-dataflow project, which itself is based on the work you did on Naiad. What was your reasoning for using Rust as the implementation target and what benefits has it provided?

What are some of the components or primitives that were missing in the Rust ecosystem as compared to what is available in Java or C/C++, which have been the dominant languages for distributed data systems?

In the list of features, you highlight full support for ANSI SQL 92. What were some of the edge cases that you faced in complying with that standard given the distributed execution context for Materialize?

A majority of SQL oriented platforms define custom extensions or built-in functions that are specific to their problem domain. What are some of the existing or

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 Distributed systems are complex to build and operate, and there are certain primitives that are common to a majority of them. Rather then re-implement the same capabilities every time, many projects build on top of Apache Zookeeper. In this episode Patrick Hunt explains how the Apache Zookeeper project was started, how it functions, and how it is used as a building block for other distributed systems. He also explains the operational considerations for running your own cluster, how it compares to more recent entrants such as Consul and EtcD, and what is in store for the future.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Patrick Hunt about Apache Zookeeper and how it is used as a building block for distributed systems

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Zookeeper is and how the project got started?

What are the main motivations for using a centralized coordination service for distributed systems?

What are the distributed systems primitives that are built into Zookeeper?

What are some of the higher-order capabilities that Zookeeper provides to users who are building distributed systems on top of Zookeeper? What are some of the types of system level features that application developers will need which aren’t provided by Zookeeper?

Can you discuss how Zookeeper is architected and how that design has evolved over time?

What have you found to be some of the most complicated or difficult aspects of building and maintaining Zookeeper?

What are the scaling factors for Zookeeper?

What are the edge cases that users should be aware of? Where does it fall on the axes of the CAP theorem?

What are the main failure modes for Zookeeper?

How much of the recovery logic is left up to the end user of the Zookeeper cluster?

Since there are a number of projects that rely on Zookeeper, many of which are likely to be run in the same environment (e.g. Kafka and Flink), what would be involved in sharing a single Zookeeper cluster among those multiple services? In recent years we have seen projects such as EtcD which is used by Kubernetes, and Consul. How does Zookeeper compare with those projects?

What are some of the cases where Zookeeper is the wrong choice?

How have the needs of distributed systems engineers changed since you first began working on Zookeeper? If you were to start the project over today, what would you do differently?

Would you still use Java?

What are some of the most interesting or unexpected ways that you have seen Zookeeper used? What do you have planned for the future of Zookeeper?

Contact Info

@phunt on Twitter

Parting Question

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

Links

Zookeeper Cloudera Google Chubby Sourceforge HBase High Availability Fallacies of distributed computing Falsehoods programmers believe about networking Consul EtcD Apache Curator Raft Consensus Algorithm Zookeeper Atomic Broadcast SSD Write Cliff Apache Kafka Apache Flink

Podcast

Summary

Modern applications and data platforms aspire to process events and data in real time at scale and with low latency. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. In this episode Fabian Hueske, one of the original authors, explains how Flink is architected, how it is being used to power some of the world’s largest businesses, where it sits in the lanscape of stream processing tools, and how you can start using it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Fabian Hueske, co-author of the upcoming O’Reilly book Stream Processing With Apache Flink, about his work on Apache Flink, the stateful streaming engine

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Flink is and how the project got started? What are some of the primary ways that Flink is used? How does Flink compare to other streaming engines such as Spark, Kafka, Pulsar, and Storm?

What are some use cases that Flink is uniquely qualified to handle?

Where does Flink fit into the current data landscape? How is Flink architected?

How has that architecture evolved? Are there any aspects of the current design that you would do differently if you started over today?

How does scaling work in a Flink deployment?

What are the scaling limits? What are some of the failure modes that users should be aware of?

How is the statefulness of a cluster managed?

What are the mechanisms for managing conflicts? What are the limiting factors for the volume of state that can be practically handled in a cluster and for a given purpose? Can state be shared across processes or tasks within a Flink cluster?

What are the comparative challenges of working with bounded vs unbounded streams of data? How do you handle out of order events in Flink, especially as the delay for a given event increases? For someone who is using Flink in their environment, what are the primary means of interacting with and developing on top of it? What are some of the most challenging or complicated aspects of building and maintaining Flink? What are some of the most interesting or unexpected ways that you have seen Flink used? What are some of the improvements or new features that are planned for the future of Flink? What are some features or use cases that you are explicitly not planning to support? For people who participate in the training sessions that you offer through Data Artisans, what are some of the concepts that they are challenged by?

What do they find most interesting or exciting?

Contact Info

LinkedIn @fhueske on Twitter fhueske on GitHub

Parting Question

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

Links

Flink Data Artisans IBM DB2 Technische Universität Berlin Hadoop Relational Database Google Cloud Dataflow Spark Cascading Java RocksDB Flink Checkpoints Flink Savepoints Kafka Pulsar Storm Scala LINQ (Language INtegrated Query) SQL Backpressure

Summary

Data integration and routing is a constantly evolving problem and one that is fraught with edge cases and complicated requirements. The Apache NiFi project models this problem as a collection of data flows that are created through a self-service graphical interface. This framework provides a flexible platform for building a wide variety of integrations that can be managed and scaled easily to fit your particular needs. In this episode project members Kevin Doran and Andy LoPresto discuss the ways that NiFi can be used, how to start using it in your environment, and plans for future development. They also explained how it fits in the broad landscape of data tools, the interesting and challenging aspects of the project, and how to build new extensions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Kevin Doran and Andy LoPresto about Apache NiFi

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what NiFi is? What is the motivation for building a GUI as the primary interface for the tool when the current trend is to represent everything as code? How did you get involved with the project?

Where does it sit in the broader landscape of data tools?

Does the data that is processed by NiFi flow through the servers that it is running on (á la Spark/Flink/Kafka), or does it orchestrate actions on other systems (á la Airflow/Oozie)?

How do you manage versioning and backup of data flows, as well as promoting them between environments?

One of the advertised features is tracking provenance for data flows that are managed by NiFi. How is that data collected and managed?

What types of reporting are available across this information?

What are some of the use cases or requirements that lend themselves well to being solved by NiFi?

When is NiFi the wrong choice?

What is involved in deploying and scaling a NiFi installation?

What are some of the system/network parameters that should be considered? What are the scaling limitations?

What have you found to be some of the most interesting, unexpected, and/or challenging aspects of building and maintaining the NiFi project and community? What do you have planned for the future of NiFi?

Contact Info

Kevin Doran

@kevdoran on Twitter Email

Andy LoPresto

@yolopey on Twitter Email

Parting Question

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

Links

NiFi HortonWorks DataFlow HortonWorks Apache Software Foundation Apple CSV XML JSON Perl Python Internet Scale Asset Management Documentum DataFlow NSA (National Security Agency) 24 (TV Show) Technology Transfer Program Agile Software Development Waterfall Spark Flink Kafka Oozie Luigi Airflow FluentD ETL (Extract, Transform, and Load) ESB (Enterprise Service Bus) MiNiFi Java C++ Provenance Kubernetes Apache Atlas Data Governance Kibana K-Nearest Neighbors DevOps DSL (Domain Specific Language) NiFi Registry Artifact Repository Nexus NiFi CLI Maven Archetype IoT Docker Backpressure NiFi Wiki TLS (Transport Layer Security) Mozilla TLS Observatory NiFi Flow Design System Data Lineage GDPR (General Data Protection Regulation)

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

Summary

The rate of change in the data engineering industry is alternately exciting and exhausting. Joe Crobak found his way into the work of data management by accident as so many of us do. After being engrossed with researching the details of distributed systems and big data management for his work he began sharing his findings with friends. This led to his creation of the Hadoop Weekly newsletter, which he recently rebranded as the Data Engineering Weekly newsletter. In this episode he discusses his experiences working as a data engineer in industry and at the USDS, his motivations and methods for creating a newsleteter, and the insights that he has gleaned from it.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Joe Crobak about his work maintaining the Data Engineering Weekly newsletter, and the challenges of keeping up with the data engineering industry.

Interview

Introduction How did you get involved in the area of data management? What are some of the projects that you have been involved in that were most personally fulfilling?

As an engineer at the USDS working on the healthcare.gov and medicare systems, what were some of the approaches that you used to manage sensitive data? Healthcare.gov has a storied history, how did the systems for processing and managing the data get architected to handle the amount of load that it was subjected to?

What was your motivation for starting a newsletter about the Hadoop space?

Can you speak to your reasoning for the recent rebranding of the newsletter?

How much of the content that you surface in your newsletter is found during your day-to-day work, versus explicitly searching for it? After over 5 years of following the trends in data analytics and data infrastructure what are some of the most interesting or surprising developments?

What have you found to be the fundamental skills or areas of experience that have maintained relevance as new technologies in data engineering have emerged?

What is your workflow for finding and curating the content that goes into your newsletter? What is your personal algorithm for filtering which articles, tools, or commentary gets added to the final newsletter? How has your experience managing the newsletter influenced your areas of focus in your work and vice-versa? What are your plans going forward?

Contact Info

Data Eng Weekly Email Twitter – @joecrobak Twitter – @dataengweekly

Parting Question

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

Links

USDS National Labs Cray Amazon EMR (Elastic Map-Reduce) Recommendation Engine Netflix Prize Hadoop Cloudera Puppet healthcare.gov Medicare Quality Payment Program HIPAA NIST National Institute of Standards and Technology PII (Personally Identifiable Information) Threat Modeling Apache JBoss Apache Web Server MarkLogic JMS (Java Message Service) Load Balancer COBOL Hadoop Weekly Data Engineering Weekly Foursquare NiFi Kubernetes Spark Flink Stream Processing DataStax RSS The Flavors of Data Science and Engineering CQRS Change Data Capture Jay Kreps

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

Summary

As software lifecycles move faster, the database needs to be able to keep up. Practices such as version controlled migration scripts and iterative schema evolution provide the necessary mechanisms to ensure that your data layer is as agile as your application. Pramod Sadalage saw the need for these capabilities during the early days of the introduction of modern development practices and co-authored a book to codify a large number of patterns to aid practitioners, and in this episode he reflects on the current state of affairs and how things have changed over the past 12 years.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Pramod Sadalage about refactoring databases and integrating database design into an iterative development workflow

Interview

Introduction How did you get involved in the area of data management? You first co-authored Refactoring Databases in 2006. What was the state of software and database system development at the time and why did you find it necessary to write a book on this subject? What are the characteristics of a database that make them more difficult to manage in an iterative context? How does the practice of refactoring in the context of a database compare to that of software? How has the prevalence of data abstractions such as ORMs or ODMs impacted the practice of schema design and evolution? Is there a difference in strategy when refactoring the data layer of a system when using a non-relational storage system? How has the DevOps movement and the increased focus on automation affected the state of the art in database versioning and evolution? What have you found to be the most problematic aspects of databases when trying to evolve the functionality of a system? Looking back over the past 12 years, what has changed in the areas of database design and evolution?

How has the landscape of tooling for managing and applying database versioning changed since you first wrote Refactoring Databases? What do you see as the biggest challenges facing us over the next few years?

Contact Info

Website pramodsadalage on GitHub @pramodsadalage on Twitter

Parting Question

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

Links

Database Refactoring

Website Book

Thoughtworks Martin Fowler Agile Software Development XP (Extreme Programming) Continuous Integration

The Book Wikipedia

Test First Development DDL (Data Definition Language) DML (Data Modification Language) DevOps Flyway Liquibase DBMaintain Hibernate SQLAlchemy ORM (Object Relational Mapper) ODM (Object Document Mapper) NoSQL Document Database MongoDB OrientDB CouchBase CassandraDB Neo4j ArangoDB Unit Testing Integration Testing OLAP (On-Line Analytical Processing) OLTP (On-Line Transaction Processing) Data Warehouse Docker QA==Quality Assurance HIPAA (Health Insurance Portability and Accountability Act) PCI DSS (Payment Card Industry Data Security Standard) Polyglot Persistence Toplink Java ORM Ruby on Rails ActiveRecord Gem

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