​Apache Iceberg™ Europe Meetup - live in Dublin!

​​Join us for the very first Apache Iceberg™ Europe Meetup in Dublin! Our event is co-hosted by Fivetran, Snowflake and Vakamo.

​🎟️ When registering, please select one of the two ticket types:

• ​In-Person Ticket: Join us on-site! Your name will be used to pre-register for venue access.

• ​Remote-Only Ticket: Can’t make it in person? No worries—register to join the live stream, receive event recordings, and stay connected with the community.

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​Agenda

​5:00 pm – Registration & Networking
6:00 pm – 1st set of short talks

​​​​​🎙️ Viktor Kessler - Apache Iceberg REST Catalog: What’s New and What’s Next
🎙️ Nicoleta Lazar - Query Federation to Apache Iceberg with StarRocks

​ Networking break

​🎙️Danica Fine - Iced Kaf-fee: Chilling Kafka Data into Iceberg Tables

​​​​​🎙️Andrew Madson - Building Agents with Apache Iceberg: Turning Data into Context for AI Systems

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​More Networking
9:00 pm – Event close

​How to Get to the Venue

​Address:

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​Building Access

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​🪪 ID Requirements

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​Presentations & Speakers

​​​​​​🌟 Apache Iceberg REST Catalog: What’s New and What’s Next

​​​​The Iceberg REST Catalog is quickly becoming the go-to way of connecting different engines and platforms to Apache Iceberg tables. But what does it actually solve, and where is the community taking it next? In this session, we’ll start with a quick intro to the REST Catalog and why it’s needed, before diving into hot topics like security, authentication, and fine-grained access control. We’ll also explore the latest developments in the community — including the new Events Endpoint — and talk about what’s coming down the road. If you’re curious about how Iceberg REST is shaping the future of open lakehouses, this is your chance to get up to speed and join the discussion.

​Viktor Kessler, is Co-Founder of Vakamo and the creator of Lakekeeper, an Apache Licensed Iceberg REST Catalog. He’s a big believer in open standards like Apache Iceberg, which he sees as the backbone of today’s modern, composable Data & Analytics systems.

​​🌟 Building Agents with Apache Iceberg: Turning Data into Context for AI Systems

​​​The rise of agents promises to revolutionize operations across verticals, but agents are failing in production. One reason is that they are fed data, but are starved of context. Foundational LLMs have broad, generalized knowledge, but they lack the specific, reliable, and up-to-date understanding of your organization's data landscape and business rules. Agents don't just need access to tables; they need to understand what the data means.
Apache Iceberg serves as an essential component for engineering context, moving from raw data to actionable knowledge.

1. Iceberg transforms chaotic data lakes into reliable information. We’ll discuss how Iceberg’s core features, such as ACID compliance for integrity, time-travel for reproducibility, and robust metadata management, are critical for consolidating siloed data into a high-performance Lakehouse trustworthy enough for AI workloads.

2. The critical leap from information to context. Structured tables alone are insufficient for LLM reasoning. We will demonstrate how a semantic layer (using frameworks like dbt Labs or the ODI partners) built on top of Iceberg tables provides the necessary business definitions, metrics, and relationships. This layer acts as the "translation engine," allowing agents to query the Lakehouse using natural language and receive governed answers.

3. The specific context needs of AI agents in production. We will cover practical strategies for leveraging an Iceberg-powered semantic layer, including optimizing Iceberg partitioning for low-latency RAG retrieval, leveraging open metadata for automated agent tool selection, and ensuring agents have the governed, accurate context required for autonomous decision-making.

You'll leave understanding how to upgrade your Iceberg implementation from a storage solution to a context engine for enterprise AI.

​​​Andrew Madson serves as the Head of Developer Relations at Fivetran and is the former Field CDO at Dremio. Andrew partners with data leaders and engineers globally to architect and implement high-performance, open data lakehouse architectures. Drawing on extensive experience spanning traditional data warehousing and modern, cloud-native data platforms, Andrew specializes in the practical application of open table formats like Apache Iceberg to solve complex enterprise challengess. Andrew a dedicated advocate for the open data ecosystem and the co-author of the O'Reilly book, Apache Polaris: The Definitive Guide, the forthcoming "Data Transformation - The Definitive Guide" and "AI-Ready Data - Turning Data Into Context For Modern AI Systems". You can find Andrew's Iceberg tutorials on LinkedIn Learning where tens of thousands have started their Apache Iceberg journey.

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​​​​​​🌟 Iced Kaf-fee: Chilling Kafka Data into Iceberg Tables

​Have piping-hot, real-time data in Apache Kafka® but want to chill it down into Apache Iceberg™ tables? Let’s see how we can craft the perfect cup of “Iced Kaf-fee” for you and your needs!
We’ll start by grinding through the motivation for moving data from Kafka topics into Iceberg tables, exploring the benefits that doing so has to offer your analytics workflows. From there, we’ll open up the menu of options available to cool down your streams, including Apache Flink®, Apache Spark™, and Kafka Connect. Each brewing method has its own recipe, so we’ll compare their pros and cons, walk through use cases for each, and highlight when you might prefer a strong Spark roast over a smooth Flink blend—or maybe a Connect cold brew. Plus, we’ll share a sneak peek at future innovations that are percolating in the community to make sinking your Kafka data into Iceberg even easier.
By the end of the session, you’ll have everything you need to whip up the perfect pipeline and serve up your “Iced Kaf-fee” with confidence.

​Danica began her career as a software engineer in financial services and pivoted to developer relations, where she focussed primarily on open source streaming and lakehouse technologies. She now leads the open source advocacy efforts at Snowflake. She can be found on X (Bluesky and Mastodon), talking about tech, plants, and baking @TheDanicaFine.

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​​​​​🌟 Query Federation to Apache Iceberg with StarRocks

​This talk introduces query federation as a way to query data in place across heterogeneous systems, then focuses on federating Apache Iceberg tables in StarRocks. We explain how StarRocks plans and executes federated queries, including metadata access and pushdown, and compare the approach with Trino, highlighting architectural trade-offs in latency, cost, and operational complexity.

Nicoleta Lazar is a Senior Data Engineer at Fresha, working on streaming pipelines, lakehouse architectures, and StarRocks. She writes and speaks about operating open-source data systems in production. Most recently, she spoke in the Databases devroom at FOSDEM, sharing practical trade-offs from real-world deployments.