Summary Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. Because of its centrality to your data systems it is valuable for debugging, governance, understanding context, and myriad other purposes. This means that it is important to have an accurate and complete lineage graph so that you don’t have to perform your own detective work when time is in short supply. In this episode Ernie Ostic shares the approach that he and his team at Manta are taking to build a complete view of data lineage across the various data systems in your organization and the useful applications of that information in the work of every data stakeholder.

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. 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. 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. Your host is Tobias Macey and today I’m interviewing Ernie Ostic about Manta, an automated data lineage service for managing visibility and quality of your data workflows

Interview

Introduction How did you get involved in the area of data management? Can you describe what Manta is and the story behind it? What are the core problems that Manta aims to solve? Data lineage and metadata systems are a hot topic right now. What i

Summary The current stage of evolution in the data management ecosystem has resulted in domain and use case specific orchestration capabilities being incorporated into various tools. This complicates the work involved in making end-to-end workflows visible and integrated. Dagster has invested in bringing insights about external tools’ dependency graphs into one place through its "software defined assets" functionality. In this episode Nick Schrock discusses the importance of orchestration and a central location for managing data systems, the road to Dagster’s 1.0 release, and the new features coming with Dagster Cloud’s general availability.

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 Nick Schrock about software defined assets and improving the developer experience for data orchestration with Dagster

Interview

Introduction How did you get involved in the area of data management? What are the notable updates in Dagster since the last time we spoke? (November, 2021) One of the core concepts that you introduced and then stabilized in recent releases is the "software defined asset" (SDA). How have your users reacted to this capability?

What are the notable outcomes in development and product practices that you have seen as a result?

What are the changes to the interfaces and internals of Dagster that were necessary to support SDA? How did the API design shift from the initial implementation once the community started providing feedback? You’re releasing the stable 1.0 version of Dagster as part of something call

Summary There are extensive and valuable data sets that are available outside the bounds of your organization. Whether that data is public, paid, or scraped it requires investment and upkeep to acquire and integrate it with your systems. Crux was built to reduce the total cost of acquisition and ownership for integrating external data, offering a fully managed service for delivering those data assets in the manner that best suits your infrastructure. In this episode Crux CTO Mark Etherington discusses the different costs involved in managing external data, how to think about the total return on investment for your data, and how the Crux platform is architected to reduce the toil involved in managing third party 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! 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. 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. Tired of deploying bad data? Need to automate data pipelines with less red tape? Shipyard is the premier data orchestration platform built to help your data team quickly launch, monitor, and share workflows in a matter of minutes. Build powerful workflows that connect your entire data stack end-to-end with a mix of your code and their open-source, low-code templates. Once launched, Shipyard makes data observability easy with logging, alerting, and retries that will catch errors before your business team does. So whether you’re ingesting data from an API, transforming it with dbt, updating BI tools, or sending data alerts, Shipyard centralizes these operations and handles the heavy lifting so your data team can finally focus on what they’re good at — solving problems with data. Go to dataengineeringpodcast.com/shipyard to get started automating with their free developer plan today! Your host is Tobias M

Summary Building a data platform is a journey, not a destination. Beyond the work of assembling a set of technologies and building integrations across them, there is also the work of growing and organizing a team that can support and benefit from that platform. In this episode Inbar Yogev and Lior Winner share the journey that they and their teams at Riskified have been on for their data platform. They also discuss how they have established a guild system for training and supporting data professionals in the 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 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. 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. Tired of deploying bad data? Need to automate data pipelines with less red tape? Shipyard is the premier data orchestration platform built to help your data team quickly launch, monitor, and share workflows in a matter of minutes. Build powerful workflows that connect your entire data stack end-to-end with a mix of your code and their open-source, low-code templates. Once launched, Shipyard makes data observability easy with logging, alerting, and retries that will catch errors before your business team does. So whether you’re ingesting data from an API, transforming it with dbt, updating BI tools, or sending data alerts, Shipyard centralizes these operations and handles the heavy lifting so your data team can finally focus on what they’re good at — solving problems with data. Go to dataengineeringpodcast.com/shipyard to get started automating with their free developer plan today! Your host is Tobias Macey and today I’m interviewing Inbar Yogev and Lior Winner about the data platform that the team at Riskified are building to power their fraud management service

Interview

Introduction How did

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 ecosystem for data tools has been going through rapid and constant evolution over the past several years. These technological shifts have brought about corresponding changes in data and platform architectures for managing data and analytical workflows. In this episode Colleen Tartow shares her insights into the motivating factors and benefits of the most prominent patterns that are in the popular narrative; data mesh and the modern data stack. She also discusses her views on the role of the data lakehouse as a building block for these architectures and the ongoing influence that it will have as the technology matures.

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. 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. Tired of deploying bad data? Need to automate data pipelines with less red tape? Shipyard is the premier data orchestration platform built to help your data team quickly launch, monitor, and share workflows in a matter of minutes. Build powerful workflows that connect your entire data stack end-to-end with a mix of your code and their open-source, low-code templates. Once launched, Shipyard makes data observability easy with logging, alerting, and retries that will catch errors before your business team does. So whether you’re ingesting data from an API, transforming it with dbt, updating BI tools, or sending data alerts, Shipyard centralizes these operations and handles the heavy lifting so your data team can finally focus on what they’re good at — solving problems with data. Go to dataengineeringpodcast.com/shipyard to get started automating with their free developer plan today! Your host is Tobias Macey and today I’m interviewing Colleen Tartow about her views on the forces shaping th

Summary The proliferation of sensors and GPS devices has dramatically increased the number of applications for spatial data, and the need for scalable geospatial analytics. In order to reduce the friction involved in aggregating disparate data sets that share geographic similarities the Unfolded team built a platform that supports working across raster, vector, and tabular data in a single system. In this episode Isaac Brodsky explains how the Unfolded platform is architected, their experience joining the team at Foursquare, and how you can start using it for analyzing your spatial data 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 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. 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. Unstruk is the DataOps platform for your unstructured data. The options for ingesting, organizing, and curating unstructured files are complex, expensive, and bespoke. Unstruk Data is changing that equation with their platform approach to manage your unstructured assets. Built to handle all of your real-world data, from videos and images, to 3d point clouds and geospatial records, to industry specific file formats, Unstruk streamlines your workflow by converting human hours into machine minutes, and automatically alerting you to insights found in your dark data. Unstruk handles data versioning, lineage tracking, duplicate detection, consistency validation, as well as enrichment through sources including machine learning models, 3rd party data, and web APIs. Go to dataengineeringpodcast.com/unstruk today to transform your messy collection of unstructured data files into actionable assets that power your business. Your host is Tobias Macey and today I’m interviewing Isaac Brodsky about Foursquare’s Unfolded platform for working w

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 Data analysis is a valuable exercise that is often out of reach of non-technical users as a result of the complexity of data systems. In order to lower the barrier to entry Ryan Buick created the Canvas application with a spreadsheet oriented workflow that is understandable to a wide audience. In this episode Ryan explains how he and his team have designed their platform to bring everyone onto a level playing field and the benefits that it provides to the 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 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. 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. Unstruk is the DataOps platform for your unstructured data. The options for ingesting, organizing, and curating unstructured files are complex, expensive, and bespoke. Unstruk Data is changing that equation with their platform approach to manage your unstructured assets. Built to handle all of your real-world data, from videos and images, to 3d point clouds and geospatial records, to industry specific file formats, Unstruk streamlines your workflow by converting human hours into machine minutes, and automatically alerting you to insights found in your dark data. Unstruk handles data versioning, lineage tracking, duplicate detection, consistency validation, as well as enrichment through sources including machine learning models, 3rd party data, and web APIs. Go to dataengineeringpodcast.com/unstruk today to transform your messy collection of unstructured data files into actionable assets that power your business. Your host is Tobias Macey and today I’m interviewing Ryan Buick about Canvas, a spreadsheet interface for your data that lets everyone on your team explore data without having to learn SQL

Interview

Introduction How did you get involved

Summary Machine learning has become a meaningful target for data applications, bringing with it an increase in the complexity of orchestrating the entire data flow. Flyte is a project that was started at Lyft to address their internal needs for machine learning and integrated closely with Kubernetes as the execution manager. In this episode Ketan Umare and Haytham Abuelfutuh share the story of the Flyte project and how their work at Union is focused on supporting and scaling the code and community that has made Flyte successful.

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. Data lake architectures provide the best combination of massive scalability and cost reduction, but they aren’t always the most performant option. That’s why Kyligence has built on top of the leading open source OLAP engine for data lakes, Apache Kylin. With their AI augmented engine they detect patterns from your critical queries, automatically build data marts with optimized table structures, and provide a unified SQL interface across your lake, cubes, and indexes. Their cost-based query router will give you interactive speeds across petabyte scale data sets for BI dashboards and ad-hoc data exploration. Stop struggling to speed up your data lake. Get started with Kyligence today at dataengineeringpodcast.com/kyligence Your host is Tobias Macey and today I’m interviewing Ketan Umare and Haytham Abuelfutuh about Flyte, the open source and kubernetes-native orchestration engine for your data systems

Interview

Introduction How did you get involved in the area of data management? Can you describe what Flyte is and the story behind it? What was missing in the ecosystem of available tools that made it necessary/worthwhile to create Flyte? Workflow orchestrators have been around for several years and have gone through a number of generational shifts. How would you characterize Flyte’s position in the ecosystem?

What do you see as the closest alternatives? What are the core differentiators that might lead someone to choose Flyte over e.g. Airflow/Prefect/Dagster?

What are the core primitives that Flyte exposes for building up complex workflows?

Machine learning use cases have been a core focus since the project’s inception. What are some of the ways that that manifests in the design and feature set?

Can you describe the architecture of Flyte?

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

What are the changes in the data ecosystem that have had the most substantial impact on the Flyte project? (e.g. roadmap, integrations, pushing people toward adoption, etc.) What is the process for setting up a Flyte deployment? What are the user personas that you prioritize in the design and feature development for Flyte? What is the workflow for someone building a new pipeline in Flyte?

What are the patterns that you and the community have established to encourage discovery and reuse of granular task definitions? Beyond code reuse, how can teams scale usage of Flyte at the company/organization level?

What are the affordances that you have created to facilitate local development and testing of workflows while ensuring a smooth transition to production?

What are the patterns that are available for CI/CD of workflows using Flyte?

How have you approached the design of data contracts/type definitions to provide a consistent/portable API for defining inter-task dependencies across languages? What are the available interfaces for extending Flyte and building integrations with other components across the data ecosystem? Data orchestration engines are a natural point for generating and taking advantage of rich metadata. How do you manage creation and propagation of metadata within and across the framework boundaries? Last year you founded Union to offer a managed version of Flyte. What are the features that you are offering beyond what is available in the open source?

What are the opportunities that you see for the Flyte ecosystem with a corporate entity to invest in expanding adoption?

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

Contact Info

Ketan Umare Haytham Abuelfutuh

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

Flyte

Slack Channel

Union.ai Kubeflow Airflow AWS Step Functions Protocol Buffers XGBoost MLFlow Dagster

Podcast Episode

Prefect

Podcast Episode

Arrow Parquet Metaflow Pytorch

Podcast.init Episode

dbt FastAPI

Podcast.init Interview

Python Type Annotations Modin

Podcast.init Interview

Monad Datahub

Podcast Episode

OpenMetadata

Podcast Episode

Hudi

Podcast Episode

Iceberg

Podcast Episode

Great Expectations

Podcast Episode

Pandera Union ML Weights and Biases Whylogs

Podcast Episode

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

Sponsored By: a…

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

Announcements

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

Interview

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

Summary 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

Support Data Engineering Podcast

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

Announcements

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

Interview

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

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

types of data quality issues identified

utility of automated vs programmatic tests

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

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

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

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

Announcements

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

Interview

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

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

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

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

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

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

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

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

Summary Transactions are a necessary feature for ensuring that a set of actions are all performed as a single unit of work. In streaming systems this is necessary to ensure that a set of messages or transformations are all executed together across different queues. In this episode Denis Rystsov explains how he added support for transactions to the Redpanda streaming engine. He discusses the use cases for transactions, the different strategies, semantics, and guarantees that they might need to support, and how his implementation ended up improving the performance of bulk write operations. This is an interesting deep dive into the internals of a high performance streaming engine and the details that are involved in building distributed 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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/impact today to save your spot at IMPACT: The Data Observability Summit a half-day virtual event featuring the first U.S. Chief Data Scientist, founder of the Data Mesh, Creator of Apache Airflow, and more data pioneers spearheading some of the biggest movements in data. The first 50 to RSVP with this link will be entered to win an Oculus Quest 2 — Advanced All-In-One Virtual Reality Headset. RSVP today – you don’t want to miss it! 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 Denis Rystsov about implementing transactions in the RedPanda streaming engine

Interview

Introduction How did you get involved in the area of data management? Can you quickly recap what RedPanda is and the goals of the project? What are the use cases for transactions in a pub/sub messaging system?

What are the elements of streaming systems that make atomic transactions a complex problem?

What was the motivation for starting down the path of adding transactions to the RedPanda engine?

How did the constraint of supporting the Kafka API influence your implementation strategy for transaction semantics?

Summary The technological and social ecosystem of data engineering and data management has been reaching a stage of maturity recently. As part of this stage in our collective journey the focus has been shifting toward operation and automation of the infrastructure and workflows that power our analytical workloads. It is an encouraging sign for the industry, but it is still a complex and challenging undertaking. In order to make this world of DataOps more accessible and manageable the team at Nexla has built a platform that decouples the logical unit of data from the underlying mechanisms so that you can focus on the problems that really matter to your business. In this episode Saket Saurabh (CEO) and Avinash Shahdadpuri (CTO) share the story behind the Nexla platform, discuss the technical underpinnings, and describe how their concept of a Nexset simplifies the work of building data products for sharing within and between organizations.

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! Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Saket Saurabh and Avinash Shahdadpuri about Nexla, a platform for powering data operations and sharing within and across businesses

Interview

Introduction How did you get involved in the area of data management? Can you describe what Nexla is and the story behind it? What are the major problems that Nexla is aiming to solve?

What are the components of a data platform that Nexla might replace?

What are the use cases and benefits of being able to publish data sets for use outside and across organizations? What are the different elements involved in implementing DataOps? How is the Nexla platform implemented?

What have been the most comple engineering challenges? How has the architecture changed or evolved since you first began working on it? What are some of the assumpt

Summary A major concern that comes up when selecting a vendor or technology for storing and managing your data is vendor lock-in. What happens if the vendor fails? What if the technology can’t do what I need it to? Compilerworks set out to reduce the pain and complexity of migrating between platforms, and in the process added an advanced lineage tracking capability. In this episode Shevek, CTO of Compilerworks, takes us on an interesting journey through the many technical and social complexities that are involved in evolving your data platform and the system that they have built to make it a manageable task.

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! Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Shevek about Compilerworks and his work on writing compilers to automate data lineage tracking from your SQL code

Interview

Introduction How did you get involved in the area of data management? Can you describe what Compilerworks is and the story behind it? What is a compiler?

How are you applying compilers to the challenges of data processing systems?

What are some use cases that Compilerworks is uniquely well suited to? There are a number of other methods and systems available for tracking and/or computing data lineage. What are the benefits of the approach that you are taking with Compilerworks? Can you describe the design and implementation of the Compilerworks platform?

How has the system changed or evolved since you first began working on it?

What programming languages and SQL dialects do you currently support?

Which have been the most challenging to work with? How do you handle verification/validation of the algebraic representation of SQL code given the variability of implementations and the flexibility of the specification?

Can you talk through the process of getting Compilerworks