Explore the future of AI-powered business intelligence with Lei Tang, CTO and Co-founder of Fabi.ai, as he discusses the evolution from traditional self-service BI to "Vibe-analytics." Learn how AI is transforming data accessibility, enabling anyone to perform sophisticated analytics without deep technical expertise. From building trust in AI-generated insights to creating intelligent semantic layers, discover how modern BI platforms are bridging the gap between data teams and business stakeholders. Tune in to understand why static dashboards are becoming obsolete and how AI agents will soon proactively surface business opportunities and insights.
Key points:

• The limitations of traditional self-service BI and how AI is addressing them
• Building secure, context-aware AI systems for data analysis
• The future of human-AI interaction in business intelligence
• Technical insights into modern BI platform architecture
• Vision for proactive, AI-driven business insights

What You'll Learn:

• Why traditional self-service BI has failed to deliver on its promises and how AI can bridge the gap
• How to build an AI-native BI platform that combines SQL, Python, and natural language processing
• The framework for implementing "Vibe-analytics" - a new paradigm of AI-powered visual analytics
• Why context engineering and semantic understanding are crucial for accurate AI-driven analysis
• How to balance security and accessibility when deploying AI-powered analytics tools
• The future of BI platforms as proactive insight generators rather than passive dashboards
• Why caching and stateful environments are essential for responsive AI-powered analytics
• How to leverage AI to translate business questions into accurate technical queries while maintaining data integrity

About the Guest(s)
Lei is the Co-founder and CTO of Fabi.ai, where he leads the development of AI-native business intelligence solutions. With a PhD in machine learning and over a decade of experience in the data domain, Lei has held significant roles, including positions at Yahoo, Walmart, Lyft (as Director of Data Science), and Clari (as Chief Data Scientist). His expertise spans machine learning, data engineering, and business analytics, with a particular focus on making data analysis more accessible and efficient. In this episode, Lei shares insights on the evolution of self-service BI and how AI is transforming business intelligence, drawing from his experience building Fabi.ai, a platform that combines SQL, Python, and AI to democratize data analysis. His work in developing "Vibe AI" (AI-powered BI) represents a significant advancement in making complex data analysis accessible to non-technical users while maintaining data accuracy and trust.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.

Quotes
"For the past decade, it's really difficult to make sure the self-service BI can work. And then now with AI, the worst part is that it can run properly, but the numbers are wrong." - Lei
"If you talk to anybody working in the BI space, like self-service BI, that has been termed for maybe for the past decade. But I have to say that is a false promise." - Lei
"We're saying that we really want those data team to be able to, like, say, what type of data is exposed to, like, say, less technical folks." - Lei
"In order to build AI native BI, I would say the focus should be how human interact with AI." - Lei
"We believe that, essentially, this BI system or, like, AI BI system would be more like a agent, and then it'll actually looking for, like, business opportunities and insight and surface to you." - Lei
"The one common theme I have been experiencing is that normally would work with other business stakeholders, could be marketing, could be operations, could be sales." - Lei
"We strongly believe that BI should be stored as code." - Lei
"Enterprise data tends to be very noisy, very complex." - Lei
"The semantics of itself becomes part of the context for the AI engine." - Lei
"Most organizations, the data, like the schema, the kind of business, like metrics and logic, has been constantly evolving." - Lei
Resources

• Fabi.ai - AI-native BI platform
• Firebolt (firebolt.io) - Cloud data warehouse platform
  
  

Tools & Technologies:

• Firebolt Core - Free self-hosted query engine
• Looker - BI Platform
• Tableau - BI Platform
• Sisense - BI Platform
• Snowflake - Data Warehouse
• BigQuery - Data Warehouse
• PostgreSQL - Database
• SQL Alchemy - Database toolkit
• Pandas - Data analysis library
  
  

For Feedback & Discussions on Firebolt Core:

• Join Firebolt Discord Community
• Join Firebolt GitHub Discussions
• Firebolt Core Github Repository 
• [email protected]

 Primary Speakers:

• Lei Tang  
• Benjamin Wagner
  
  

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

Journey inside Uber's innovative AI assistant "Genie" with Paarth Chotani, Staff Engineer at Uber, as he shares how they're revolutionizing on-call support using LLMs and vector search. From processing massive amounts of internal documentation to building scalable RAG pipelines, discover how Uber tackles the challenges of implementing AI assistants at scale. Get insights into the evolution from traditional chatbots to agent-based solutions, and learn practical lessons about staying current in the rapidly evolving AI landscape. Whether you're building AI-powered tools or scaling data infrastructure, this episode offers valuable perspectives on balancing innovation with real-world implementation.
• Building and scaling RAG pipelines at enterprise scale• Evolution from traditional chatbots to AI agents• Practical insights on data processing and vector search implementation• Leveraging open-source technologies in production environments• Navigating rapid technological changes in AI development
What You'll Learn:

• How Uber transformed its on-call support system by building an AI assistant that searches across internal documentation, wikis, and code
• Why combining multiple data sources with vector databases creates more accurate and contextual responses for enterprise support
• The evolution from basic RAG implementation to agent-based architecture for handling complex support scenarios
• How to scale AI processing pipelines using Apache Spark for large-scale data chunking and embedding generation
• Why customization and internal data sources are crucial for enterprise AI assistant effectiveness
• The future of AI assistants: moving from documentation lookup to automated problem resolution through multi-agent systems
• How to balance rapid AI innovation with setting realistic customer expectations in fast-moving tech environments
  
  

Paarth is a Staff Engineer at Uber, where he works on Michelangelo, Uber's machine learning platform. With over four years at Uber, he specializes in feature store development, online serving at scale, and GenAI implementations. He has been instrumental in developing Genie, an AI-powered on-call assistant that revolutionizes how Uber's engineering teams handle support requests and documentation access. In this episode, Paarth shares valuable insights on building and scaling RAG-based systems, vector search implementations, and the evolution of AI assistants from traditional chatbots to sophisticated agent-based solutions. His experience spanning both AWS chatbot development and current GenAI innovations at Uber offers listeners a unique perspective on the rapid advancement of AI-powered enterprise solutions.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.

Quotes
"Think of Genie as your on-call assistant. Different infra teams have their Slack channels, and because these technologies are widely used, you have to wait a lot." - Paarth

"What we realized is for our engineers to really get help, data sources really should be internal only because we customize lot of these open source engines for making it work at Uber scale." - Paarth

"Instead of building a mega scale pipeline that just ingest all data sources and then keeps a central data source solution, we instead are giving users the flexibility to ingest what data sources they want." - Paarth

"We had to scale our you can say the whole infrared layer to chunk data faster to be able to create embedding set scale." - Paarth

"It almost felt like they're doing what EMR was doing. You have your Hadoop and big data technology, and we needed these pipelines to basically process all this data quickly." - Paarth

"We've even evolved from just giving you the right documentation to starting to evolve into a situation where we'll also start taking actions on your behalf." - Paarth

"That intuition that comes from building this kind of bot, I feel like that intuition came again as we were starting to see this technology come, and we're like, hey, this looks like where you can pretty much fit all these pieces together." - Paarth

"What we have seen with several use cases is agentic genie works well when designed well, when you've analyzed the problem of which type of subproblems the bot should resolve per channel, per use case." - Paarth

"I think having a problem in mind always helps that way, the energy is little bit focused and directed." - Paarth

"Whatever you're building is not enough because the expectation has already gone to the next level, so the pace is too fast right now." - Paarth


Resources

• Companies & Platforms:
• Uber - ML Platform & Engineering
• Firebolt - Cloud Data Warehouse (firebolt.io)
  
  

Tools & Technologies:

• Michelangelo - Uber's ML Platform 
• Genie - Uber's On-Call Assistant Bot
• Cursor - Developer IDE
• OpenSearch - Vector Database
• LangGraph - Agent Framework
  
  

Notable Projects Mentioned:

• MetaMate (Meta)
• Query Copilot (Uber)
• Scale at AI (Meta Meetup)
  
  

Company Blogs:

• Uber Engineering Blog - Genie and Query Optimization articles
  
  

 Primary Speakers:

• Paarth Chotani - Staff Engineer, Uber
• Benjamin - Firebolt
• Eldad - Firebolt

For Feedback & Discussions on Firebolt Core:

• Join Firebolt Discord Community
• Join Firebolt GitHub Discussions
• Firebolt Core Github Repository 
• [email protected]

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

AI's transformative impact on data engineering and analytics is reshaping how professionals create value, shifting focus from technical skills to strategic thinking and communication.
In this episode of The Data Engineering Show, the bros talk with Sumit Gupta, Lead BI Engineer at Notion, about his journey through prominent tech companies, modern data stacks, and how AI is revolutionizing data workflows and professional development.
What You'll Learn:

• How modern data stacks are evolving with tools like Snowflake, dbt, Iceberg, and Hex
• Why transferable skills are becoming more crucial than technical expertise in the AI era
• How to leverage AI tools strategically
• The framework for automating content creation workflows using AI tools and APIs
• Why this is "the worst AI will ever be" and how to prepare for accelerating change
• How to balance AI automation with authentic human connection in content creation
• Why modern data professionals must embrace AI while maintaining ethical considerations
• How companies like Notion are implementing AI for improved customer insights and engagement

This episode offers valuable insights into the practical application of AI in data workflows, content creation, and professional development, while addressing both the opportunities and challenges in the evolving tech landscape.
Highlights:

[03:19] - Modern Data Stack Evolution for Scale

[10:05] - AI-Powered Customer Intelligence Platform

[15:17] - Future of Data Careers in the AI Era

[18:49] - Automated Content Creation Workflow

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.

About the Guest
Sumit Gupta is a Lead BI Engineer at Notion, where he spearheads reporting and dashboarding initiatives for marketing and sales teams. With over a decade of experience in data and analytics, including notable roles at industry leaders like Snowflake and Dropbox, he brings deep expertise in modern data stack implementation and AI integration. In this episode, Sumit shares valuable insights on the evolution of data engineering, the impact of AI on analytics workflows, and how he leverages various AI tools to enhance productivity both professionally and as a content creator with 21,000+ Instagram followers. His unique perspective on balancing technical expertise with transferable skills in the age of AI, combined with his experience at "Bay Area Darlings" like Notion, Snowflake, and Dropbox, makes this conversation particularly relevant for data professionals navigating the rapidly evolving tech landscape.
Quotes
"The scariest part about the whole AI boom is this is the worst AI will ever be." - Sumit
"If you are someone who's starting new in data field, the value of your technical skills that used to be very valuable until 2021 is not as much - your transferable skills or soft skills comes into picture." - Sumit
"Every bit is expensive - all the servers are cheap, but when you're dealing with hundred million users and trillions of rows of data a day, you have to find that one percent saving." - Sumit
"AI has made me a lot more productive, but at the same time, it has also made me dumber." - Sumit
"If you are someone new, especially in data, scared of AI or skeptic of AI, I would say jump in - if you don't jump onto the bandwagon right now, you might be left out in a year or so." - Sumit
Resources

• Sumit Gupta LinkedIn
• Notion Website
• Sumit Gupta Instagram
• Firebolt Website

For Feedback & Discussions on Firebolt Core:

• Join Firebolt Discord Community
• Join Firebolt GitHub Discussions
• Firebolt Core Github Repository 
• [email protected]

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

In this episode of The Data Engineering Show, host Benjamin and co-host Eldad sit with Yingjun Wu, founder and CEO of Rising Wave, to explore the evolution of stream processing systems and the innovations his company is bringing to the space.
What you’ll learn:

• Yingjun's journey from academic research in stream processing to founding Rising Wave, and the challenges of building trust in a new database system.
  
  
• How Rising Wave's architecture, using S3 as primary storage, delivers second-level scalability, while other systems can take hours to scale.
  
  
• The competitive landscape of stream processing, with Rising Wave's Postgres compatibility providing a significant advantage in ease of use.
  
  
• How one major company reduced its CPU requirements from 20,000 to just 600 by switching from a traditional stream processing system to Rising Wave.
  
  
• The rising importance of Apache Iceberg as a destination for stream processing output, helping companies avoid vendor lock-in.
  
  
• How streaming systems fit into modern data stacks, especially as companies seek to avoid being locked into proprietary systems.
  
  

Yingjun Wu is the founder and CEO of Rising Wave, a stream processing system built in Rust and designed with a cloud-native architecture. With a PhD focused on stream processing and database systems, Yingjun previously worked at Redshift and IBM Research before founding Rising Wave. His company has developed a system that achieves significant performance and resource efficiency advantages over traditional stream processing solutions, while maintaining Postgres compatibility for ease of use.

Episode Highlights:

The Origins of Rising Wave (00:30)

Yingjun shares his background in stream processing from his PhD days and explains how his experience at Redshift revealed the need for better stream processing solutions, especially since many data warehouse workloads involve data ingested from streaming sources like Kinesis or Kafka.

Building a System from Scratch (04:10)

Yingjun describes the challenging first 2-3 years of developing Rising Wave without customers, highlighting how trust is a major barrier for new database systems. After 2.5 years, they secured their first customers, including a startup and several larger companies, which helped establish Rising Wave's credibility.

The Current Stream Processing Landscape (07:47)

Benjamin asks about the current stream processing space, with Yingjun positioning Rising Wave as a leader, particularly for SQL-based workloads. He highlights several key advantages of Rising Wave, including its Rust-based implementation and S3-based storage architecture.

S3 as Primary Storage (10:27)

Yingjun explains their decision to use S3 as primary storage from day one, despite its slowness and expense. He discusses how they've optimized for these challenges and would still make the same architectural choice today due to benefits like simplified state management and superior elastic scaling.

The Business Model (13:52)

Rising Wave offers open-source, cloud, and on-premise versions of its product. Yingjun notes that many highly regulated industries require on-premise deployment, including customers in the banking and aerospace sectors.

Typical Users and Competitive Advantages (15:01)

When asked about their typical users, Yingjun explains they directly compete with Flink but have advantages in ease of use due to Postgres compatibility. Their users are either new to stream processing or are migrating from systems like Spark Streaming or Flink due to performance issues or development complexity.

Apache Iceberg Integration (19:25)

Yingjun discusses how Apache Iceberg is emerging as an important destination for Rising Wave output, as companies seek to avoid vendor lock-in with proprietary data warehouses. He explains how Rising Wave typically performs ETL functions before data is sent to Iceberg tables.

The Future of Data Management (32:06)

The conversation concludes with a discussion about Iceberg becoming a "single source of truth" for data, with multiple specialized query engines potentially accessing the same data. Yingjun and Eldad share perspectives on how this shift away from proprietary data lock-in is changing the data ecosystem.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.

Episode Resources:

• Rising Wave Website
• Yingjun Wu LinkedIn

For Feedback & Discussions on Firebolt Core:

• Join Firebolt Discord Community
• Join Firebolt GitHub Discussions
• Firebolt Core Github Repository 
• [email protected]

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

In this episode of The Data Engineering Show, the bros sit with Lisa Cao, Product Manager at DataStrato, to explore data catalogs and Apache Gravitino, a unified metadata lake used to manage access and perform data governance for all data sources. 
What You’ll Learn:

• How Apache Gravitino differs from others like Unity catalog and Polaris by being able to support multiple catalog systems.
• What the “Push-Down Permission Management” security model is and how to implement it across different data systems. 
• How to maintain consistent governance across various query engines like Spark, Trino, and Flink.
• Why interoperability, flexibility and open source ecosystem are becoming an important dynamics of data infrastructure rather than performance benchmarking.
• How to evaluate new data tools based on their real-world adoption rather than the social media hype.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts instructions on how to do this here [insert link].

Lisa Cao is a Product Manager at DataStrato, specializing in AI/ML product partnerships and developer relations. With deep expertise in data catalog technologies and open-source ecosystems, she plays a key role in developing Apache Gravitino, an ASF incubating project that provides a unified governance and security layer for diverse data systems. Her work in developing extensible catalog frameworks has helped organizations manage complex data environments across multiple platforms.
Episode Highlights:

• What is Apache Gravitino? (01:24)

Apache Gravitino is a meta-catalog that serves as a unified data governance and security layer used to manage different data systems. Lisa shares that Gravitino was the first to release an iceberg rest catalog and ended up open sourcing for the general community to use and as time passed, Polaris and Unity Catalog were also announced in open source. She highlights that although Gravitino, Polaris and Unity Catalog are very similar, Gravitino differs in that it is able to support multiple catalogs.

• Unifying AI/ML and Big Data Stack (03:15)

One of the interesting things about Gravitino is that it offers more than just a catalog of data models and these model catalogs are the first step into looking at how to merge two worlds of AI and ML catalogs. Lisa shares the goal of effective management, that is, creating a system that can store and manage different types of data models, track changes to the models, and control access to the models.

• Simplifying Data Governance (10:49)

Think of Gravitino as a “traffic cop” that helps to manage and secure data from multiple sources. It is crucial to have a system that provides unified access control across all data sources, allowing teams to manage access and data governance so that ML teams don't have to worry about access. Lisa says that Apache Gravitino is the system that makes data accessible to different teams and users while making sure that it is secure and governed appropriately. 

• The Gravitino’s Query Engine Solution (21:34)

Every query engine has its own way of managing data, which makes it difficult to switch between engines - you have to reconfigure everything. Lisa highlights that Gravitino solves the problem by providing a single layer of data governance that works across multiple query engines.

• Navigating the Fast-Paced World of Data Engineering (24:41)

Lisa talks about how fast the data engineering space is moving and shares some insights to catching up;

• Don’t try to learn everything at once.
• Don't get too deep into every tool
• Look for real-world adoption

She warns against the social media hype that can amplify the messaging around new tools, making it seem everyone is using it, when in reality, that can’t be easily seen.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.
Episode Resources:

• Apache Gravitino website

For Feedback & Discussions on Firebolt Core:

• Join Firebolt Discord Community
• Join Firebolt GitHub Discussions
• Firebolt Core Github Repository 
• [email protected]

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

In this episode of The Data Engineering Show, host Benjamin and co-host Eldad sit with CEO DuckDB Labs and co-creator DuckDB, Hannes Mühleisen.
Together, they:

• Talk about the journey of DuckDB, an open-source analytical database system designed as a universal wrangling tool.
• Explain how DuckDB differs from SQLite, highlighting the analytical and transactional use cases.
• Discuss DuckDB’s special feature and its approach to innovation including creating their Parquet Reader.
• Explore the simple and efficient ecosystem of DuckDB, allowing developers to add custom functionality without changing its core stability.
• Consider Hannes' perspective on the role of AI in databases.
• Delve into the system’s infrastructure, design choices and the dedication of the team to ensure a continuous, reliable database system.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts, instructions on how to do this are [insert link].
Hannes Mühleisen is the CEO of DuckDB Labs and a Professor in The Netherlands, renowned for co-creating DuckDB, an open-source analytical database system. With a background in database architecture and research from CWI database architectures group, he has pioneered the development of DuckDB as a universal data wrangling tool that can run everywhere from phones to space satellites. Under his leadership, DuckDB has achieved remarkable success, reaching 10 million downloads monthly and becoming a go-to solution for analytical database needs. His commitment to keeping DuckDB lightweight, portable, and hardware-agnostic while maintaining high performance has revolutionized how developers approach analytical database solutions. As both an academic and technology leader, Hannes brings unique insights into database architecture, open-source development, and the future of analytical data processing.
Episode Highlights:

• The Purpose of DuckDB (01:04)

Hannes gives a full description of what DuckDB is as well as what it is designed to do. He describes the tool as one that understands SQL and is specifically designed to simplify complex analytical use cases.

• SQLite vs DuckDB (02:53)

Hannes compares two different tools stating that SQLite is an amazing system that is not meant for analytical queries but for transactional use cases while DuckDB is specifically designed for that exact purpose - analytical use cases. 

• The Importance of Collaboration (08:14)

Hannes states the need for community collaboration as the database engine space seems to have hundreds of brilliant people trying to solve the same problems. He shares his profound admiration for a team in Munich, praising them for their exploits in implementing concepts only described in paper.

• The Component-Based Architecture of DuckDB (11:25)

Hannes highlights a special feature in DuckDB, that is, it can be used as a component and he explains that the in-process architecture is a success because of the memory of data sharing that can be achieved.

• The Parquet Reader Journey (17:51)

Hannes explains how he built his Parquet Reader out of necessity, although he would have preferred not to. He shares how a creator named Ove Korn from Germany donated the reader to a project named “The Arrow Project” and managed it to the degree that the entire project depended on the use of the Parquet Reader and it became an issue to use both independently. Hannes adds that a parquet reader that is competent has no choice but to become a database engine which is one of the interesting things about development.

• The Role of AI in Database Interaction (22:41)

Hannes states that he doesn’t think that AI has a place in a database engine but rather, it is needed for optimization because the researchers who built their careers on optimization are out of jobs. He explains that the role of AI should be for assistance tasks and not for a total execution.

• SQL - A Defined Interface (29:20)

Hannes introduces us to a tool that allows us to pro-programmatically build a query called relational API stating that it helps to simplify the tasks of a programmer. Although, Hannes agrees that using a well-defined interface is important for components like databases, he also argues that SQL can provide a relatively defined behavior within a single system. 

• The Golden Age of Database (38:57)

Hannes concludes the episode by appreciating Firebolt and other engineers for taking on core engine tasks. He shares his excitement for the golden age of databases where there is a showcasing of what is possible.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.

Quotes:

1. “DuckDB is a universal data wrangling tool. It is a relational data management system that speaks SQL designed to do well on analytical use cases.”

1. “We call ourselves the SQLite for analytics because it explains the original design goal of DuckDB very well.”

1. “Within the database engine space, we are all working to solve the same problems, and that's like, a hundred of us on the planet.”

1. “It actually turns out in order to make a competent parquet reader, you do need query execution. There is just no way around it.”

1. “I really like this golden age of databases we are in and personally, as somebody who really likes tables and SQL, I'm quite happy to see things like firebolt and others really working on core engine stuff.”

For Feedback & Discussions on Firebolt Core:

• Join Firebolt Discord Community
• Join Firebolt GitHub Discussions
• Firebolt Core Github Repository 
• [email protected]

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

In this episode of The Data Engineering Show, the bros sit with Daniel Pálma, Head of Marketing at Estuary.
Join them as they:

• Talk about Daniel’s career transition from data engineering to marketing and how his background in data engineering has been a tremendous help to his marketing competence.
• Discuss the role of AI in the evolution of data movement ensuring a faster and easier process of creating data pipelines.
• Shine light on the challenges of vector databases and structured data in AI applications.
• Delve into the future of Apache Iceberg and data lakehouses, highlighting their current challenges.
• Shares insights on the golden age of data expressing the need for more data engineers, data analysts and data practitioners in the data space.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts, instructions on how to do this are here.
Daniel Pálma serves as Head of Marketing at Estuary, bringing a unique blend of technical expertise and marketing acumen to the data integration space. With nearly a decade of experience as a data engineer across startups, enterprises, and consulting roles, Daniel made a strategic pivot to marketing to help bridge the gap between complex technical solutions and their practical applications for data practitioners. His background in data engineering enables him to deeply understand the customers' challenges and create authentic, education-focused marketing content that resonates with technical audiences. Daniel’s thought leadership and content creation in the data engineering space, combined with his hands-on technical experience, positions him as a valuable voice in conversations about the evolution of data infrastructure and integration technologies.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.
For Feedback & Discussions on Firebolt Core:

• Join Firebolt Discord Community
• Join Firebolt GitHub Discussions
• Firebolt Core Github Repository 
• [email protected]

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

In this episode of The Data Engineering Show, host Benjamin and co-host Eldad sit with Chad Sanderson, CEO and co-founder of Gable AI  to explore the interesting world of data change management.
Join them as they:

• Delve into challenges of data quality, how it degrades over time and the one-sided data quality checks on the “last mile” of the data supply chain.
• Talk about how Gable works through a 3-layer flow of technology which is to identify data production points, trace the data flow and communicate the impact of changes before they reach production.
• Explain why the gap between data producers and consumers need to be bridged and how Gable continues to emphasize the need for effective communication and understanding data change management across teams
• Shine light on how AI can enhance data management by extracting semantics from code and effectively manage the translation output.
• Discuss Chad’s vision for 2025 which is to help companies start to care about data and how the changes made to data affect other people.

Chad Sanderson is the CEO and co-founder of Gable AI, a data change management platform. Chad has over a decade of experience in data engineering and infrastructure space, holding significant roles at major companies like Microsoft, Oracle, Sephora where he focused on data quality and governance challenges. He is a former Head of Data at Convoy, a LinkedIn writer, and a published author. He lives in Seattle, Washington, and is the Chief Operator of the Data Quality Camp. His journey from data scientist to data engineer and ultimately to CEO was driven by a desire to transform how organizations manage and utilize data. Gable AI addresses the complexities of the data supply chain, by providing tools for code scanning, data contracts and governance as code, enabling teams to proactively manage data changes and impact.

If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.
Episode Resources

• Gable AI website
• Chad Sanderson on LinkedIn

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The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

Wouter Trappers is the founder of Xudo and shares his slightly unconventional path from philosopher to data consultant with the Bros in this latest episode of The Data Engineering Show. Wouter’s grounding in philosophy has proved to be a shaping influence on his approach to business intelligence. Much more than just a software solution, for Wouter, BI is all about change management and aligning leadership with data projects.


They discuss:

• 
  From Excel to Expert: From basic Excel tasks to a full mastery of BI tools like QlikView, Wouter has blended his technical and philosophical approaches to data to become a bona fide expert.
• Data Strategy as Transformation: Good change management principles have to be adhered to if a BI project is going to bear fruit. Focus on leadership alignment, KPI clarity, and user empowerment instead of simply implementing software. 
• Challenges of Starting Small: Wouter has some tips to offer smaller companies around bootstrapping their data journey using existing tools, practical education, and even Gen AI.
• Balancing Scales: Smaller startups compared to large enterprises face a very different set of challenges.
  
  

Wouter’s combination of philosophy and pragmatism brings fresh takes to building effective data solutions.



The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing

In this special roundup episode of The Data Engineering Show, the Bros revisits some of the best bits from episodes with data thought leaders Zach Wilson, Matthew Housley, Joe Reis, and Krishnan Viswanathan, spotlighting essential trends and lessons learned across the evolving data engineering landscape. From data observability to bridging academia with real-world practice, this episode covers perspectives on where data engineering is heading and why certain challenges persist.


Topics include:

• Foundations of Data Engineering: Zach Wilson emphasizes the importance of core, tech-agnostic skills in data modeling, quality assurance, and storytelling. By sharing his experiences at Airbnb and in education, he reveals that effective data engineering hinges on creating robust data models, quality controls, and persuasive narratives rather than expertise in any single tool or language.
• Bridging Academia and Practice: Matthew Housley and Joe Reis delve into the need for better data education, emphasizing hands-on experience and data fundamentals over tool-specific training, and advocate for apprenticeships and real-world collaborations in educational settings.
• Legacy Meets Modern in Data Engineering: Krishnan Viswanathan reflects on recurring themes in data engineering and the importance of adapting legacy approaches to new data needs, underscoring the challenges and benefits of vendor-built versus in-house solutions.
  
  

Join the Bros for a well-rounded exploration of current themes in data engineering, filled with practical advice for data professionals at any stage of their journey.



The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so

Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.

Check out our three most downloaded episodes:

• Zach Wilson on What Makes a Great Data Engineer
• Joe Reis and Matt Housley on The Fundamentals of Data Engineering
• Bill Inmon, The Godfather of Data Warehousing