The lakehouse is built for storage flexibility, but what about compute? In this session, we’ll explore how Unity Catalog enables you to connect and govern multiple compute engines across your data ecosystem. With open APIs and support for the Iceberg REST Catalog, UC lets you extend access to engines like Trino, DuckDB, and Flink while maintaining centralized security, lineage, and interoperability. We will show how you can get started today working with engines like Apache Spark and Starburst to read and write to UC managed tables with some exciting demos. Learn how to bring flexibility to your compute layer—without compromising control.

DoorDash Data organization actively adopts LakeHouse paradigm. This presentation describes the methodology which allows to migrate the classic Data Warehouse and Data Lake platforms to unified LakeHouse solution.The objective of this effort include Elimination of excessive data movement.Seamless integration and consolidation of the query engine layers, including Snowflake, Databricks, EMR and Trino.Query performance optimization.Abstracting away complexity of underlying storage layers and table formatsStrategic and justified decision on the Unified Metadata catalog used across varios compute platforms

Moving data between operational systems and analytics platforms is often painful. Traditional pipelines become complex, brittle, and expensive to maintain.Take Kafka and Iceberg: batching on Kafka causes ingestion bottlenecks, while streaming-style writes to Iceberg create too many small Parquet files—cluttering metadata, degrading queries, and increasing maintenance overhead. Frequent updates further strain background table operations, causing retries—even before dealing with schema evolution. But much of this complexity is avoidable. What if Kafka Topics and Iceberg Tables were treated as two sides of the same coin? By establishing a transparent equivalence, we can rethink pipeline design entirely. This session introduces Tableflow—a new approach to bridging streaming and table-based systems. It shifts complexity away from pipelines and into a unified layer, enabling simpler, declarative workflows. We’ll cover schema evolution, compaction, topic-to-table mapping, and how to continuously materialize and optimize thousands of topics as Iceberg tables. Whether modernizing or starting fresh, you’ll leave with practical insights for building resilient, scalable, and future-proof data architectures.

Unity Catalog puts variety of schemas into a centralized repository, now the developer community wants more productivity and automation for schema inference, translation, evolution and optimization especially for the scenarios of ingestion and reverse-ETL with more code generations.Coinbase Data Platform attempts to pave a path with "Schemaster" to interact with data catalog with the (proposed) metadata model to make schema translation and evolution more manageable across some of the popular systems, such as Delta, Iceberg, Snowflake, Kafka, MongoDB, DynamoDB, Postgres...This Lighting Talk covers 4 areas: The complexity and caveats of schema differences among The proposed field-level metadata model, and 2 translation patterns: point-to-point vs hub-and-spoke Why Data Profiling be augmented to enhance schema understanding and translation Integrate it with Ingestion & Reverse-ETL in a Databricks-oriented eco system Takeaway: standardize schema lineage & translation

Data is the backbone of modern decision-making, but centralizing it is only the tip of the iceberg. Entitlements, secure sharing and just-in-time availability are critical challenges to any large-scale platform. Join Goldman Sachs as we reveal how our Legend Lakehouse, coupled with Databricks, overcomes these hurdles to deliver high-quality, governed data at scale. By leveraging an open table format (Apache Iceberg) and open catalog format (Unity Catalog), we ensure platform interoperability and vendor neutrality. Databricks Unity Catalog then provides a robust entitlement system that aligns with our data contracts, ensuring consistent access control across producer and consumer workspaces. Finally, Legend functions, integrating with Databricks User Defined Functions (UDF), offer real-time data enrichment and secure transformations without exposing raw datasets. Discover how these components unite to streamline analytics, bolster governance and power innovation.

In this presentation, we'll show how we achieved a unified development experience for teams working on Mercedes-Benz Data Platforms in AWS and Azure. We will demonstrate how we implemented Azure to AWS and AWS to Azure data product sharing (using Delta Sharing and Cloud Tokens), integration with AWS Glue Iceberg tables through UniForm and automation to drive everything using Azure DevOps Pipelines and DABs. We will also show how to monitor and track cloud egress costs and how we present a consolidated view of all the data products and relevant cost information. The end goal is to show how customers can offer the same user experience to their engineers and not have to worry about which cloud or region the Data Product lives in. Instead, they can enroll in the data product through self-service and have it available to them in minutes, regardless of where it originates.

Get a first look at multi-statement transactions in Databricks. In this session, we will dive into their capabilities, exploring how multi-statement transactions enable atomic updates across multiple tables in your data pipelines, ensuring data consistency and integrity for complex operations. We will also share how we are enabling unified transactions across Delta Lake and Iceberg with Unity Catalog — powering our vision for an open and interoperable lakehouse.

Open table formats such as Apache Iceberg or Delta Lake have transformed the data landscape. For the first time, we’re seeing a real open storage ecosystem emerging across database vendors. So far, open table formats have found little adoption powering low-latency, high-concurrency analytics use-cases. Data stored in open formats often gets transformed and ingested into closed systems for serving. The reason for this is simple: most modern query engines don’t properly support these workloads. In this talk we take a look under the hood of Firebolt and dive into the work we’re doing to support low-latency and high concurrency on Iceberg: caching of data and metadata, adaptive object storage reads, subresult reuse, and multi-dimensional scaling. After this session, you will know how you can build low-latency data applications on top of Iceberg. You’ll also have a deep understanding of what it takes for modern high-performance query engines to do well on these workloads.

Summary In this episode of the Data Engineering Podcast Alex Albu, tech lead for AI initiatives at Starburst, talks about integrating AI workloads with the lakehouse architecture. From his software engineering roots to leading data engineering efforts, Alex shares insights on enhancing Starburst's platform to support AI applications, including an AI agent for data exploration and using AI for metadata enrichment and workload optimization. He discusses the challenges of integrating AI with data systems, innovations like SQL functions for AI tasks and vector databases, and the limitations of traditional architectures in handling AI workloads. Alex also shares his vision for the future of Starburst, including support for new data formats and AI-driven data exploration tools.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th. This episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial.Your host is Tobias Macey and today I'm interviewing Alex Albu about how Starburst is extending the lakehouse to support AI workloadsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining the interaction points of AI with the types of data workflows that you are supporting with Starburst?What are some of the limitations of warehouse and lakehouse systems when it comes to supporting AI systems?What are the points of friction for engineers who are trying to employ LLMs in the work of maintaining a lakehouse environment?Methods such as tool use (exemplified by MCP) are a means of bolting on AI models to systems like Trino. What are some of the ways that is insufficient or cumbersome?Can you describe the technical implementation of the AI-oriented features that you have incorporated into the Starburst platform?What are the foundational architectural modifications that you had to make to enable those capabilities?For the vector storage and indexing, what modifications did you have to make to iceberg?What was your reasoning for not using a format like Lance?For teams who are using Starburst and your new AI features, what are some examples of the workflows that they can expect?What new capabilities are enabled by virtue of embedding AI features into the interface to the lakehouse?What are the most interesting, innovative, or unexpected ways that you have seen Starburst AI features used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI features for Starburst?When is Starburst/lakehouse the wrong choice for a given AI use case?What do you have planned for the future of AI on Starburst?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 [email protected] with your story.Links StarburstPodcast EpisodeAWS AthenaMCP == Model Context ProtocolLLM Tool UseVector EmbeddingsRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeStarburst Data ProductsLanceLanceDBParquetORCpgvectorStarburst IcehouseThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

This session showcases our journey of adopting Apache Iceberg™ to build a modern lakehouse architecture and leveraging Databricks advanced Iceberg support to take it to the next level. We’ll dive into the key design principles behind our lakehouse, the operational challenges we tackled and how Databricks enabled us to unlock enhanced performance, scalability and streamlined data workflows. Whether you’re exploring Apache Iceberg™ or building a lakehouse on Databricks, this session offers actionable insights, lessons learned and best practices for modern data engineering.

In this session, learn about why modern open table formats like Delta and Iceberg are a big deal and how they work with catalogs. Learn about what motivated their creation, how they work, what benefits they can bring to your data and AI platform. Hear about how these formats are becoming increasingly interoperable and what our vision is for their future.

This session introduces ByteDance’s challenges in data management and model training, and addresses them by Magnus (enhanced Apache Iceberg) and Byted Streaming (customized Mosaic Streaming). Magnus uses Iceberg’s branch/tag to manage massive datasets/checkpoints efficiently. With enhanced metadata and a custom C++ data reader, Magnus achieves optimal sharding, shuffling and data loading. Flexible table migration, detailed metrics and built-in full-text indexes on Iceberg tables further ensure training reliability. When training with ultra-large datasets, ByteDance faced scalability and performance issues. Given Streaming's scalability in distributed training and good code structure, the team chose and customized it to resolve challenges like slow startup, high resource consumption, and limited data source compatibility. In this session, we will explore Magnus and Byted Streaming, discuss their enhancements and demonstrate how they enable efficient and robust distributed training.

Table formats like Delta Lake and Iceberg have been game changers for pushing lakehouse architecture into modern Enterprises. The acquisition of Tabular added Iceberg to the Databricks ecosystem, an open format that was already well supported by processing engines across the industry. At HelloFresh we are building a lakehouse architecture that integrates many touchpoints and technologies all across the organization. As such we chose Iceberg as the table format to bridge the gaps in our decentralized managed tech landscape. We are leveraging Unity Catalog as the Iceberg REST catalog of choice for storing metadata and managing tables. In this talk we will outline our architectural setup between Databricks, Spark, Flink and Snowflake and will explain the native Unity Iceberg REST catalog, as well as catalog federation towards connected engines. We will highlight the impact on our business and discuss the advantages and lessons learned from our early adopter experience.

As data ecosystems grow more complex, organizations often struggle with siloed platforms and fragmented governance. In this session, we’ll explore how our team made Databricks the central hub for cross-platform interoperability, enabling seamless Snowflake integration through Unity Catalog and the Iceberg REST API. We’ll cover: Why interoperability matters and the business drivers behind our approach How Unity Catalog and Uniform simplify interoperability, allowing Databricks to expose an Iceberg REST API for external consumption Technical deep dive into data sharing, query performance, and access control across Databricks and Snowflake Lessons learned and best practices for building a multi-engine architecture while maintaining governance and efficiency By leveraging Uniform, Delta, and Iceberg, we created a flexible, vendor-agnostic architecture that bridges Databricks and Snowflake without compromising performance or security.

NHS England is revolutionizing healthcare research by enabling secure, seamless access to de-identified patient data through the Federated Data Platform (FDP). Despite vast data resources spread across regional and national systems, analysts struggle with fragmented, inconsistent datasets. Enter Databricks: powering a unified, virtual data lake with Unity Catalog at its core — integrating diverse NHS systems while ensuring compliance and security. By bridging AWS and Azure environments with a private exchange and leveraging the Iceberg connector to interface with Palantir, analysts gain scalable, reliable and governed access to vital healthcare data. This talk explores how this innovative architecture is driving actionable insights, accelerating research and ultimately improving patient outcomes.

With hundreds of millions viewing broadcasts from news to sports, Fox relies on a sophisticated and trusted architecture ingesting 100+ data sources, carefully governed to improve UX across products, drive sales and marketing, and ensure KPI tracking. Join Oliver Gomes, VP of Enterprise and Data Platform at Fox, and Prukalpa Sankar of Atlan to learn how true partnership helps their team navigate opportunities from Governance to AI. To govern and democratize their multi-cloud data platform, Fox chose Atlan to make data accessible and understandable for more users than ever before. Their team then used a data product approach to create a shared language using context from sources like Unity Catalog at a single point of access, no matter the underlying technology. Now, Fox is defining an ambitious future for Metadata. With Atlan and Iceberg driving interoperability, their team prepares to build a “control plane”, creating a common system of trust and governance.

As data architectures evolve to meet the demands of real-time GenAI applications, organizations increasingly need systems that unify streaming and batch processing while maintaining compatibility with existing tools. The Ursa Engine offers a Kafka-API-compatible data streaming engine built on Lakehouse (Iceberg and Delta Lake). Designed to seamlessly integrate with data lakehouse architectures, Ursa extends your lakehouse capabilities by enabling streaming ingestion, transformation and processing — using a Kafka-compatible interface. In this session, we will explore how Ursa Engine augments your existing lakehouses with Kafka-compatible capabilities. Attendees will gain insights into Ursa Engine architecture and real-world use cases of Ursa Engine. Whether you're modernizing legacy systems or building cutting-edge AI-driven applications, discover how Ursa can help you unlock the full potential of your data.

Explore how AI-powered Generative Agents can evolve in real time using live data streams. Inspired by Stanford's 'Generative Agents' paper, this session dives into building dynamic, AI-driven worlds with Apache Kafka, Flink, and Iceberg - plus LLMs, RAG, and Python. Demos and practical examples included!

You've probably heard the name Apache Iceberg by now. If it wasn't when Databricks reportedly spent 2 billion USD buying Tabular, it might have been when AWS announced S3 Tables built on Iceberg. But do you know what Apache Iceberg actually is? Or how you could start using it today?

In this tutorial, we will walk through an end-to-end example of writing and reading Iceberg data, while taking a few pitstops to demonstrate Iceberg's selling points.

Hello, my fair-weathered friends and readers! I am gone on vacation this week with my family, probably at this moment lying in the sand on a beach (Lord willing the creek don’t rise), not thinking of you all. Anywho, be that as it may, I didn’t want you to miss my pretty face, so here is a video of me ranting about Apache Iceberg, something I’ve had a lot of practice doing and enjoy quite thoroughly. For all you free-loaders out there, you can get 20% off to celebrate Memorial Day. https://dataengineeringcentral.substack.com/Merica

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