431 episodes

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

Data Engineering Podcast Tobias Macey

    • Technology
    • 4.6 • 12 Ratings

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

    Being Data Driven At Stripe With Trino And Iceberg

    Being Data Driven At Stripe With Trino And Iceberg

    Summary

    Stripe is a company that relies on data to power their products and business. To support that functionality they have invested in Trino and Iceberg for their analytical workloads. In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform.


    Announcements


    Hello and welcome to the Data Engineering Podcast, the show about modern data management
    Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
    Your host is Tobias Macey and today I'm interviewing Kevin Liu about his use of Trino and Iceberg for Stripe's data lakehouse


    Interview


    Introduction
    How did you get involved in the area of data management?
    Can you describe what role Trino and Iceberg play in Stripe's data architecture?


    What are the ways in which your job responsibilities intersect with Stripe's lakehouse infrastructure?

    What were the requirements and selection criteria that led to the selection of that combination of technologies?


    What are the other systems that feed into and rely on the Trino/Iceberg service?

    what kinds of questions are you answering with table metadata


    what use case/team does that support

    comparative utility of iceberg REST catalog
    What are the shortcomings of Trino and Iceberg?
    What are the most interesting, innovative, or unexpected ways that you have seen Iceberg/Trino used?
    What are the most interesting, unexpected, or challenging lessons that you have learned while working on Stripe's data infrastructure?
    When is a lakehouse on Trino/Iceberg the wrong choice?
    What do you have planned for the future of Trino and Iceberg at Stripe?


    Contact Info


    Substack
    LinkedIn


    Parting Question


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


    Closing Announcements


    Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
    Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.


    Links


    Trino
    Iceberg
    Stripe
    Spark
    Redshift
    Hive Metastore
    Python Iceberg
    Python Iceberg REST Catalog
    Trino Metadata Table
    Flink


    Podcast Episode

    Tabular


    Podcast Episode

    Delta Table


    Podcast Episode

    Databricks Unity Catalog
    Starburst
    AWS Athena
    Kevin Trinofest Presentation
    Alluxio


    Podcast Episode

    Parquet
    Hudi
    Trino Project Tardigrade
    Trino On Ice


    The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
    Sponsored By:
    Starburst: ![Starburst Logo](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/UpvN7wDT.png)

    This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, the query engine Apache Iceberg was designed for, Starburst is an open platform with support for all table formats including Apache Iceberg, Hive, and Delta Lake.

    Trusted by the teams at Comcast and Doordash, Starburst

    • 53 min
    X-Ray Vision For Your Flink Stream Processing With Datorios

    X-Ray Vision For Your Flink Stream Processing With Datorios

    Summary

    Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. To address this shortcoming Datorios created an observability platform for Flink that brings visibility to the internals of this popular stream processing system. In this episode Ronen Korman and Stav Elkayam discuss how the increased understanding provided by purpose built observability improves the usefulness of Flink.


    Announcements


    Hello and welcome to the Data Engineering Podcast, the show about modern data management
    This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support.
    Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
    Your host is Tobias Macey and today I'm interviewing Ronen Korman and Stav Elkayam about pulling back the curtain on your real-time data streams by bringing intuitive observability to Flink streams


    Interview


    Introduction
    How did you get involved in the area of data management?
    Can you describe what Datorios is and the story behind it?
    Data observability has been gaining adoption for a number of years now, with a large focus on data warehouses. What are some of the unique challenges posed by Flink?


    How much of the complexity is due to the nature of streaming data vs. the architectural realities of Flink?

    How has the lack of visibility into the flow of data in Flink impacted the ways that teams think about where/when/how to apply it?
    How have the requirements of generative AI shifted the demand for streaming data systems?


    What role does Flink play in the architecture of generative AI systems?

    Can you describe how Datorios is implemented?


    How has the design and goals of Datorios changed since you first started working on it?

    How much of the Datorios architecture and functionality is specific to Flink and how are you thinking about its potential application to other streaming platforms?
    Can you describe how Datorios is used in a day-to-day workflow for someone building streaming applications on Flink?
    What are the most interesting, innovative, or unexpected ways that you have seen Datorios used?
    What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datorios?
    When is Datorios the wrong choice?
    What do you have planned for the future of Datorios?


    Contact Info


    Ronen


    LinkedIn

    Stav


    LinkedIn



    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

    • 42 min
    Practical First Steps In Data Governance For Long Term Success

    Practical First Steps In Data Governance For Long Term Success

    Summary

    Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Data governance is the binding force between these two parts of the organization. Nicola Askham found her way into data governance by accident, and stayed because of the benefit that she was able to provide by serving as a bridge between the technology and business. In this episode she shares the practical steps to implementing a data governance practice in your organization, and the pitfalls to avoid.


    Announcements


    Hello and welcome to the Data Engineering Podcast, the show about modern data management
    Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
    This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support.
    Your host is Tobias Macey and today I'm interviewing Nicola Askham about the practical steps of building out a data governance practice in your organization


    Interview


    Introduction
    How did you get involved in the area of data management?
    Can you start by giving an overview of the scope and boundaries of data governance in an organization?


    At what point does a lack of an explicit governance policy become a liability?

    What are some of the misconceptions that you encounter about data governance?
    What impact has the evolution of data technologies had on the implementation of governance practices? (e.g. number/scale of systems, types of data, AI)
    Data governance can often become an exercise in boiling the ocean. What are the concrete first steps that will increase the success rate of a governance practice?


    Once a data governance project is underway, what are some of the common roadblocks that might derail progress?

    What are the net benefits to the data team and the organization when a data governance practice is established, active, and healthy?
    What are the most interesting, innovative, or unexpected ways that you have seen data governance applied?
    What are the most interesting, unexpected, or challenging lessons that you have learned while working on data governance/training/coaching?
    What are some of the pitfalls in data governance?
    What are some of the future trends in data governance that you are excited by?


    Are there any trends that concern you?



    Contact Info


    Website
    LinkedIn


    Parting Question


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


    Closing Announcements


    Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it

    • 1 hr
    Data Migration Strategies For Large Scale Systems

    Data Migration Strategies For Large Scale Systems

    Summary

    Any software system that survives long enough will require some form of migration or evolution. When that system is responsible for the data layer the process becomes more challenging. Sriram Panyam has been involved in several projects that required migration of large volumes of data in high traffic environments. In this episode he shares some of the valuable lessons that he learned about how to make those projects successful.


    Announcements


    Hello and welcome to the Data Engineering Podcast, the show about modern data management
    Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
    This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support.
    Your host is Tobias Macey and today I'm interviewing Sriram Panyam about his experiences conducting large scale data migrations and the useful strategies that he learned in the process


    Interview


    Introduction
    How did you get involved in the area of data management?
    Can you start by sharing some of your experiences with data migration projects?


    As you have gone through successive migration projects, how has that influenced the ways that you think about architecting data systems?

    How would you categorize the different types and motivations of migrations?


    How does the motivation for a migration influence the ways that you plan for and execute that work?

    Can you talk us through one or two specific projects that you have taken part in?
    Part 1: The Triggers


    Section 1: Technical Limitations triggering Data Migration


    Scaling bottlenecks: Performance issues with databases, storage, or network infrastructure
    Legacy compatibility: Difficulties integrating with modern tools and cloud platforms
    System upgrades: The need to migrate data during major software changes (e.g., SQL Server version upgrade)

    Section 2: Types of Migrations for Infrastructure Focus


    Storage migration: Moving data between systems (HDD to SSD, SAN to NAS, etc.)
    Data center migration: Physical relocation or consolidation of data centers
    Virtualization migration: Moving from physical servers to virtual machines (or vice versa)

    Section 3: Technical Decisions Driving Data Migrations


    End-of-life support: Forced migration when older software or hardware is sunsetted
    Security and compliance: Adopting new platforms with better security postures
    Cost Optimization: Potential savings of cloud vs. on-premise data centers


    Part 2: Challenges (and Anxieties)


    Section 1: Technical Challenges


    Data transformation challenges: Schema changes, complex data mappings
    Network bandwidth and latency: Transferring large datasets efficiently
    Performance

    • 1 hr
    Zenlytic Is Building You A Better Coworker With AI Agents

    Zenlytic Is Building You A Better Coworker With AI Agents

    Summary

    The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. In this episode they share their journey through the fast-moving landscape of generative AI and unpack the difference between an AI chatbot and an AI agent.


    Announcements


    Hello and welcome to the Data Engineering Podcast, the show about modern data management
    This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support.
    Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
    Your host is Tobias Macey and today I'm interviewing Ryan Janssen and Paul Blankley about their experiences building AI powered agents for interacting with your data


    Interview


    Introduction
    How did you get involved in data? In AI?
    Can you describe what Zenlytic is and the role that AI is playing in your platform?
    What have been the key stages in your AI journey?


    What are some of the dead ends that you ran into along the path to where you are today?
    What are some of the persistent challenges that you are facing?

    So tell us more about data agents. Firstly, what are data agents and why do you think they're important?
    How are data agents different from chatbots?
    Are data agents harder to build? How do you make them work in production?
    What other technical architectures have you had to develop to support the use of AI in Zenlytic?
    How have you approached the work of customer education as you introduce this functionality?
    What are some of the most interesting or erroneous misconceptions that you have heard about what the AI can and can't do?
    How have you balanced accuracy/trustworthiness with user experience and flexibility in the conversational AI, given the potential for these models to create erroneous responses?
    What are the most interesting, innovative, or unexpected ways that you have seen your AI agent used?
    What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI agent for business intelligence?
    When is an AI agent the wrong choice?
    What do you have planned for the future of AI in the Zenlytic product?


    Contact Info


    Ryan


    LinkedIn

    Paul


    LinkedIn



    Parting Question


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


    Closing Annou

    • 54 min
    Release Management For Data Platform Services And Logic

    Release Management For Data Platform Services And Logic

    Summary

    Building a data platform is a substrantial engineering endeavor. Once it is running, the next challenge is figuring out how to address release management for all of the different component parts. The services and systems need to be kept up to date, but so does the code that controls their behavior. In this episode your host Tobias Macey reflects on his current challenges in this area and some of the factors that contribute to the complexity of the problem.


    Announcements


    Hello and welcome to the Data Engineering Podcast, the show about modern data management
    This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support.
    Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
    Your host is Tobias Macey and today I want to talk about my experiences managing the QA and release management process of my data platform


    Interview


    Introduction
    As a team, our overall goal is to ensure that the production environment for our data platform is highly stable and reliable. This is the foundational element of establishing and maintaining trust with the consumers of our data. In order to support this effort, we need to ensure that only changes that have been tested and verified are promoted to production.
    Our current challenge is one that plagues all data teams. We want to have an environment that mirrors our production environment that is available for testing, but it’s not feasible to maintain a complete duplicate of all of the production data. Compounding that challenge is the fact that each of the components of our data platform interact with data in slightly different ways and need different processes for ensuring that changes are being promoted safely.


    Contact Info


    LinkedIn
    Website


    Closing Announcements


    Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
    Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.


    Links


    Data Platforms and Leaky Abstractions Episode
    Building A Data Platform From Scratch
    Airbyte


    Podcast Episode

    Trino
    dbt
    Starburst Galaxy
    Superset
    Dagster
    LakeFS


    Podcast Episode

    Nessie


    Podcast Episode

    Iceberg
    Snowflake
    LocalStack
    DSL == Domain Specific Language


    The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC B

    • 20 min

Customer Reviews

4.6 out of 5
12 Ratings

12 Ratings

GreatStuff123 ,

The missing Data Engineering Podcast!

I just found out about this podcast while browsing Twitter and seeing that the host of another of my favourite podcasts (Tobias Macey from Podcast.__Init__) had a new podcast on data engineering.

With the demise of several older Hadoop podcasts and O'Reilley's more buisiness-focused data podcast, a new series like this one was sorely needed for discussions of current data architectures and pipelines.

Thanks and keep up the good work Tobias, I've already learned so much after binging the first several podcasts! Looking forward to the next interviews.

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