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 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 Building a well rounded and effective data team is an iterative process, and the first hire can set the stage for future success or failure. Trupti Natu has been the first data hire multiple times and gone through the process of building teams across the different stages of growth. In this episode she shares her thoughts and insights on how to be intentional about establishing your own data team.

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. 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 vali

We talked about:

Christopher’s background The essence of DataOps Also known as Agile Analytics Operations or DevOps for Data Science Defining processes and automating them (defining “done” and “good”) The balance between heroism and fear (avoiding deferred value) The Lean approach Avoiding silos The 7 steps to DataOps Wanting to become replaceable DataOps is doable Testing tools DataOps vs MLOps The Head Chef at Data Kitchen What’s grilling at Data Kitchen? The DataOps Cookbook

Links:

DataOps Manifesto website: https://dataopsmanifesto.org/en/ DataOps Cookbook: https://dataops.datakitchen.io/pf-cookbook Recipes for DataOps Success: https://dataops.datakitchen.io/pf-recipes-for-dataops-success DataOps Certification Course: https://info.datakitchen.io/training-certification-dataops-fundamentals DataOps Blog: https://datakitchen.io/blog/ DataOps Maturity Model: https://datakitchen.io/dataops-maturity-model/ DataOps Webinars: https://datakitchen.io/webinars/

Join DataTalks.Club: https://datatalks.club/slack.html  

Our events: https://datatalks.club/events.html

Summary Putting machine learning models into production and keeping them there requires investing in well-managed systems to manage the full lifecycle of data cleaning, training, deployment and monitoring. This requires a repeatable and evolvable set of processes to keep it functional. The term MLOps has been coined to encapsulate all of these principles and the broader data community is working to establish a set of best practices and useful guidelines for streamlining adoption. In this episode Demetrios Brinkmann and David Aponte share their perspectives on this rapidly changing space and what they have learned from their work building the MLOps community through blog posts, podcasts, and discussion forums.

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. Your host is Tobias Macey and today I’m interviewing Demetrios Brinkmann and David Aponte about what you need to know about MLOps as a data engineer

Interview

Introduction How did you get involved in the area of data management? Can you describe what MLOps is?

How does it relate to DataOps? DevOps? (is it just another buzzword?)

What is your interest and involvement in the space of MLOps? What are the open and active questions in the MLOps community? Who is responsible for MLOps in an organization?

What is the role of the data engineer in that process?

What are the core capabilities that are necessary to support an "MLOps" workflow? How do the current platform technologies support the adoption of MLOps workflows?

What are the areas that are currently underdeveloped/underserved?

Can you describe the technical and organizational design/architecture decisions that need to be made when endeavoring to adopt MLOps practices? What are some of the common requirements for supporting ML workflows?

What are some of the ways that requirements become bespoke to a given organization or project?

What are the opportunities for standardization or consolidation in the tooling for MLOps?

What are the pieces that are always going to require custom engineering?

What are the most interesting, innovative, or unexpected approaches to MLOps workflows/platforms that you have seen? What are the most interesting, unexpected, or challenging lessons that you

Summary Data engineering is a practice that is multi-faceted and requires integration with a large number of systems. This often means working across multiple tools to get the job done which can introduce significant cost to productivity due to the number of context switches. Rivery is a platform designed to reduce this incidental complexity and provide a single system for working across the different stages of the data lifecycle. In this episode CEO and founder Itamar Ben hemo explains how his experiences in the industry led to his vision for the Rivery platform as a single place to build end-to-end analytical workflows, including how it is architected and how you can start using it today for your own work.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription 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. Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now! Your host is Tobias Macey and today I’m interviewing Itamar Ben Hemo about Rivery, a SaaS platform designed to provide an end-to-end solution for Ingestion, Transformation, Orchestration,

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

Announcements

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

Interview

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

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

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

What are some of the ways t

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

Announcements

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

Interview

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

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

Announcements

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

Interview

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

How the project came about Goals of the book

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

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

Announcements

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

Interview

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

What are the goals and purpose of these contracts?

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

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

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

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

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m interviewing Brian McMillan about building data products and his book to introduce the work of data analysts and engineers to non-programmers

Interview

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

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

What

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

Announcements

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

Interview

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

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

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

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

Summary The data that you have access to affects the questions that you can answer. By using external data sources you can drastically increase the range of analysis that is available to your organization. The challenge comes in all of the operational aspects of finding, accessing, organizing, and serving that data. In this episode Mark Hookey discusses how he and his team at Demyst do all of the DataOps for external data sources so that you don’t have to, including the systems necessary to organize and catalog the various collections that they host, the various serving layers to provide query interfaces that match your platform, and the utility of having a single place to access a multitude of information. If you are having trouble answering questions for your business with the data that you generate and collect internally, then it is definitely worthwhile to explore the information available from external sources.

Announcements

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

Interview

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

What are the services and systems that you provide for organizations to incorporate external sources in their data workflows? Who are your target customers?

What are some examples of data sets that an organization might want to use in their analytics?

How are these different from SaaS data that an organization might integrate with tools such as Stitcher and Fivetran?

What are some of the challenges that are introduced by working with these external data sets?

If an organization isn’t using Demyst what are some

Summary The accuracy and availability of data has become critically important to the day-to-day operation of businesses. Similar to the practice of site reliability engineering as a means of ensuring consistent uptime of web services, there has been a new trend of building data reliability engineering practices in companies that rely heavily on their data. In this episode Egor Gryaznov explains how this practice manifests from a technical and organizational perspective and how you can start adopting it in your own teams.

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 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. Your host is Tobias Macey and today I’m interviewing Egor Gryaznov, co-founder and CTO of Bigeye, about the ideas and practices of data reliability engineering and how to integrate it into your systems

Interview

Introduction How did you get involved in the area of data management? What does the term "Data Reliability Engineering" mean? What is encompassed under the umbrella of Data Reliability Engineering?

How does it compare to the concepts from site reliability engineering? Is DRE just a repackaged version of DataOps?

Why is Data Reliability Engineering particularly important now? Who is responsible for the practice of DRE in an organization? What are some areas of innovation that teams are focusing on to support a DRE practice? What are the tools that teams are using to improve the reliability of their data operations? What are the organizational systems that need to be in place to support a DRE practice?

What are some potential roadblocks that teams might have to address when planning and implementing a DRE strategy?

What are the most interesting, innovative, or unexpected approaches/solutions to DRE that you have seen? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Data Reliability Engineering? Is Data Reliability Engi

Summary Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. Add the work of creating the model itself, and it’s not surprising that a majority of companies that could greatly benefit from machine learning have yet to either put it into production or see the value. Tristan Zajonc recognized the complexity that acts as a barrier to adoption and created the Continual platform in response. In this episode he shares his perspective on the benefits of declarative machine learning workflows as a means of accelerating adoption in businesses that don’t have the time, money, or ambition to build everything from scratch. He also discusses the technical underpinnings of what he is building and how using the data warehouse as a shared resource drastically shortens the time required to see value. This is a fascinating episode and Tristan’s work at Continual is likely to be the catalyst for a new stage in the machine learning 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! 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. 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 Tristan Zajonc about Continual, a platform for automating the creation and application of operational AI on top of your data warehouse

Interview

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

What is your definition for "operational AI" and how does it differ from other applications of ML/AI?

What are some example use cases for AI in an operational capacity?

What are the barriers to adoption for organizations that want to take advantage of predictive analytics?

Who are the target users of Continual? Can you describe how the Continual platform is implemented?

How has the design and infrastructure changed or evolved since you first began working on it?

What is the workflow for

Summary The Cassandra database is one of the first open source options for globally scalable storage systems. Since its introduction in 2008 it has been powering systems at every scale. The community recently released a new major version that marks a milestone in its maturity and stability as a project and database. In this episode Ben Bromhead, CTO of Instaclustr, shares the challenges that the community has worked through, the work that went into the release, and how the stability and testing improvements are setting the stage for the future of the project.

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. 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 Ben Bromhead about the recent release of Cassandra version 4 and how it fits in the current landscape of data tools

Interview

Introduction How did you get involved in the area of data management? For anyone who isn’t familiar with Cassandra, can you briefly describe what it is and some of the story behind it?

How did you get involved in the Cassandra project and how would you characterize your role?

What are the main use cases and industries where someone is likely to use Cassandra? What is notable about the version 4 release?

What were some of the factors that contributed to the long delay between versions 3 and 4? (2015 – 2021) What are your thoughts on the ongoing utility/benefits of projects such as ScyllaDB, particularly in light of the most recent release?

Cassandra is primarily used as a system of record. What are some of the tools and system architectures that users turn to when building analytical workloads for data stored in Cassandra? The architecture of Cassandra has lent itself well to the cloud native ecosystem that has been growing in recent years. What do you see as the opportunities for Cassandra over the near to medium term as the cloud continues to grow in prominence?

Summary Gartner analysts are tasked with identifying promising companies each year that are making an impact in their respective categories. For businesses that are working in the data management and analytics space they recognized the efforts of Timbr.ai, Soda Data, Nexla, and Tada. In this episode the founders and leaders of each of these organizations share their perspective on the current state of the market, and the challenges facing businesses and data professionals 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 Have you ever had to develop ad-hoc solutions for security, privacy, and compliance requirements? Are you spending too much of your engineering resources on creating database views, configuring database permissions, and manually granting and revoking access to sensitive data? Satori has built the first DataSecOps Platform that streamlines data access and security. Satori’s DataSecOps automates data access controls, permissions, and masking for all major data platforms such as Snowflake, Redshift and SQL Server and even delegates data access management to business users, helping you move your organization from default data access to need-to-know access. Go to dataengineeringpodcast.com/satori today and get a $5K credit for your next Satori subscription. Your host is Tobias Macey and today I’m interviewing Saket Saurabh, Maarten Masschelein, Akshay Deshpande, and Dan Weitzner about the challenges facing data practitioners today and the solutions that are being brought to market for addressing them, as well as the work they are doing that got them recognized as "cool vendors" by Gartner.

Interview

Introduction How did you get involved in the area of data management? Can you each describe what you view as the biggest challenge facing data professionals? Who are you building your solutions for and what are the most common data management problems are you all solving? What are different components of Data Management and why is it so complex? What will simplify this process, if any? The report covers a lot of new data management terminology – data governance, data observability, data fabric, data mesh, DataOps, MLOps, AIOps – what does this all mean and why is it important for data engineers? How has the data management space changed in recent times? Describe the current data management landscape and any key developments. From your perspective, what are the biggest challenges in the data management space today? What modern data management features are lacking in existing databases? Gartner imagines a future where data and analytics leaders need to be prepared to rely on data manage

Summary The Presto project has become the de facto option for building scalable open source analytics in SQL for the data lake. In recent months the community has focused their efforts on making it the fastest possible option for running your analytics in the cloud. In this episode Dipti Borkar discusses the work that she and her team are doing at Ahana to simplify the work of running your own PrestoDB environment in the cloud. She explains how they are optimizin the runtime to reduce latency and increase query throughput, the ways that they are contributing back to the open source community, and the exciting improvements that are in the works to make Presto an even more powerful option for all of your analytics.

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. 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 Dipti Borkar, cofounder Ahana about Presto and Ahana, SaaS managed service for Presto

Interview

Introduction How did you get involved in the area of data management? Can you describe what Ahana is and the story behind it? There has been a lot of recent activity in the Presto community. Can you give an overview of the options that are available for someone wanting to use its SQL engine for querying their data?

What is Ahana’s role in the community/ecosystem? (happy to skip this question if it’s too contentious) What are some of the notable differences that have emerged over the past couple of years between the Trino (formerly PrestoSQL) and PrestoDB projects?

Another area that has been seeing a lot of activity is data lakes and projects to make them more manageable and feature complete (e.g. Hudi, Delta Lake, Iceberg, Nessie, LakeFS, etc.). How has that influenced your product focus and capabilities?

How does this activity change the calculus for organizations who are deciding on a lake or warehouse for their data architecture?

Can y

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

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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