Data Engineering Podcast Tobias Macey

Summary
Generative AI has rapidly gained adoption for numerous use cases. To support those applications, organizational data platforms need to add new features and data teams have increased responsibility. In this episode Lior Gavish, co-founder of Monte Carlo, discusses the various ways that data teams are evolving to support AI powered features and how they are incorporating AI into their work.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData 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 Lior Gavish about the impact of AI on data engineersInterview
IntroductionHow did you get involved in the area of data management?Can you start by clarifying what we are discussing when we say "AI"?Previous generations of machine learning (e.g. deep learning, reinforcement learning, etc.) required new features in the data platform. What new demands is the current generation of AI introducing?Generative AI also has the potential to be incorporated in the creation/execution of data pipelines. What are the risk/reward tradeoffs that you have seen in practice?What are the areas where LLMs have proven useful/effective in data engineering?Vector embeddings have rapidly become a ubiquitous data format as a result of the growth in retrieval augmented generation (RAG) for AI applications. What are the end-to-end operational requirements to support this use case effectively?As with all data, the reliability and quality of the vectors will impact the viability of the AI application. What are the different failure modes/quality metrics/error conditions that they are subject to?As much as vectors, vector databases, RAG, etc. seem exotic and new, it is all ultimately shades of the same work that we have been doing for years. What are the areas of overlap in the work required for running the current generation of AI, and what are the areas where it diverges?What new skills do data teams need to acquire to be effective in supporting AI applications?What are the most interesting, innovative, or unexpected ways that you have seen AI impact data engineering teams?What are the most interesting, unexpected, or challenging lessons that you have learned while working with the current generation of AI?When is AI the wrong choice?What are your predictions for the future impact of AI on data engineering teams?Contact Info
LinkedInParting 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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.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 Links
Monte CarloPodcast EpisodeNLP == Natural Language ProcessingLarge Language ModelsGenerative AIMLOpsML EngineerFeature StoreRetrieval Augmented Generation (RAG)LangchainThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary
In this episode Praveen Gujar, Director of Product at LinkedIn, talks about the intricacies of product management for data and analytical platforms. Praveen shares his journey from Amazon to Twitter and now LinkedIn, highlighting his extensive experience in building data products and platforms, digital advertising, AI, and cloud services. He discusses the evolving role of product managers in data-centric environments, emphasizing the importance of clean, reliable, and compliant data. Praveen also delves into the challenges of building scalable data platforms, the need for organizational and cultural alignment, and the critical role of product managers in bridging the gap between engineering and business teams. He provides insights into the complexities of platformization, the significance of long-term planning, and the necessity of having a strong relationship with engineering teams. The episode concludes with Praveen offering advice for aspiring product managers and discussing the future of data management in the context of AI and regulatory compliance.

Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData 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 Praveen Gujar about product management for data and analytical platformsInterview
IntroductionHow did you get involved in the area of data management?Product management is typically thought of as being oriented toward customer facing functionality and features. What is involved in being a product manager for data systems?Many data-oriented products that are customer facing require substantial technical capacity to serve those use cases. How does that influence the process of determining what features to provide/create?investment in technical capacity/platformsidentifying groupings of features that can be served by a common platform investmentmanaging organizational pressures between engineering, product, business, finance, etc.What are the most interesting, innovative, or unexpected ways that you have seen "Data Products & Platforms @ Big-tech" used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on "Building Data Products & Platforms for Big-tech"?When is "Data Products & Platforms @ Big-tech" the wrong choice?What do you have planned for the future of "Data Products & Platforms @ Big-tech"?Contact Info
LinkedInWebsiteParting 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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.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
DataHubPodcast EpisodeRAG == Retrieval Augmented GenerationThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary
Postgres is one of the most widely respected and liked database engines ever. To make it even easier to use for developers to use, Nikita Shamgunov decided to makee it serverless, so that it can scale from zero to infinity. In this episode he explains the engineering involved to make that possible, as well as the numerous details that he and his team are packing into the Neon service to make it even more attractive for anyone who wants to build on top of Postgres.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData 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 Nikita Shamgunov about his work on making Postgres a serverless database at Neon.Interview
IntroductionHow did you get involved in the area of data management?Can you describe what Neon is and the story behind it?The ecosystem around Postgres is large and varied. What are the pain points that you are trying to address with Neon? What does it mean for a database to be serverless?What kinds of products and services are unlocked by making Postgres a serverless database?How does your vision for Neon compare/contrast with what you know of PlanetScale?Postgres is known for having a large ecosystem of plugins that add a lot of interesting and useful features, but the storage layer has not been as easily extensible historically. How have architectural changes in recent Postgres releases enabled your work on Neon?What are the core pieces of engineering that you have had to complete to make Neon possible?How have the design and goals of the project evolved since you first started working on it?The separation of storage and compute is one of the most fundamental promises of the cloud. What new capabilities does that enable in Postgres?How does the branching functionality change the ways that development teams are able to deliver and debug features?Because the storage is now a networked system, what new performance/latency challenges does that introduce? How have you addressed them in Neon?Anyone who has ever operated a Postgres instance has had to tackle the upgrade process. How does Neon address that process for end users?The rampant growth of AI has touched almost every aspect of computing, and Postgres is no exception. How does the introduction of pgvector and semantic/similarity search functionality impact the adoption and usage patterns of Postgres/Neon?What new challenges does that introduce for you as an operator and business owner?What are the lessons that you learned from MemSQL/SingleStore that have been most helpful in your work at Neon?What are the most interesting, innovative, or unexpected ways that you have seen Neon used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Neon?When is Neon the wrong choice? Postgres?What do you have planned for the future of Neon?Contact Info
@nikitabase on TwitterLinkedInParting 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, an

Summary
This episode features an insightful conversation with Petr Janda, the CEO and founder of Synq. Petr shares his journey from being an engineer to founding Synq, emphasizing the importance of treating data systems with the same rigor as engineering systems. He discusses the challenges and solutions in data reliability, including the need for transparency and ownership in data systems. Synq's platform helps data teams manage incidents, understand data dependencies, and ensure data quality by providing insights and automation capabilities. Petr emphasizes the need for a holistic approach to data reliability, integrating data systems into broader business processes. He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data managementData 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 Petr Janda about Synq, a data reliability platform focused on leveling up data teams by supporting a culture of engineering rigorInterview
IntroductionHow did you get involved in the area of data management?Can you describe what Synq is and the story behind it? Data observability/reliability is a category that grew rapidly over the past ~5 years and has several vendors focused on different elements of the problem. What are the capabilities that you saw as lacking in the ecosystem which you are looking to address?Operational/infrastructure engineers have spent the past decade honing their approach to incident management and uptime commitments. How do those concepts map to the responsibilities and workflows of data teams? Tooling only plays a small part in SLAs and incident management. How does Synq help to support the cultural transformation that is necessary?What does an on-call rotation for a data engineer/data platform engineer look like as compared with an application-focused team?How does the focus on data assets/data products shift your approach to observability as compared to a table/pipeline centric approach?With the focus on sharing ownership beyond the boundaries on the data team there is a strong correlation with data governance principles. How do you see organizations incorporating Synq into their approach to data governance/compliance?Can you describe how Synq is designed/implemented? How have the scope and goals of the product changed since you first started working on it?For a team who is onboarding onto Synq, what are the steps required to get it integrated into their technology stack and workflows?What are the types of incidents/errors that you are able to identify and alert on? What does a typical incident/error resolution process look like with Synq?What are the most interesting, innovative, or unexpected ways that you have seen Synq used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Synq?When is Synq the wrong choice?What do you have planned for the future of Synq?Contact Info
LinkedInSubstackParting 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 fr

Summary

Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service.


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 Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou


Interview


Introduction
How did you get involved in the area of data management?
Can you describe what Microsoft Fabric is and the story behind it?
Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend?
Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution?


What are the elements of Fabric that were engineered specifically for the service?
What are the most interesting/complicated integration challenges?

How has your prior experience with Ahana and Presto informed your current work at Microsoft?
AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine?


What are the challenges in terms of safety and reliability?

What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically?
When is Fabric the wrong choice?
What do you have planned for the future of data lake analytics?


Contact Info


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


Microsoft Fabric
Ahana episode
DB2 Distributed
Spark
Presto
Azure Data
MAD Landscape


Podcast Episode
ML Podcast Episode

Tableau
dbt
Medallion Architecture
Microsoft Onelake
ORC
Parquet
Avro
Delta Lake
Iceberg


Podcast Episode

Hudi


Podcast Episode

Hadoop
PowerBI


Podcast Episode

Velox
Gluten
Apache XTable
GraphQL
Formula 1
McLaren


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

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 b

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: 

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