Apache Iceberg™ Europe Community Meetup - Dec 2025 Paris Edition
Apache Iceberg™ Europe Meetup - live in Paris!
Join us for the Apache Iceberg™ Europe Meetup in Paris! Our event is co-hosted by DataDog, Synaltic , Dremio and Vakamo.
Livestream
https://datadog.zoom.us/j/94471341424?pwd=OxzC2lMrEXRAXUS5ICGriud4WmEjNb.1
Also make sure to join Apache Iceberg Slack Channel to stay up-to-date with future meetups in Europe!
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
🎙️ Firas Omrane and Alessandro Nori Inside Iceberg's Commit Path: Corruption, Latency, and Observability at Datadog
🎙️ JB Onofré Apache Polaris (incubating), on-prem storage and more
7:20 pm – 7:40 pm Networking break
🎙️Andrew Madson - Building Agents with Apache Iceberg: Turning Data into Context for AI Systems
🎙️ Charly CLAIRMONT - 1000+ Columns and 5ms Latency: Building a Data Lakehouse for Flight Simulation with Apache Iceberg
8:15 pm – More Networking
9:00 pm – Event close
How to Get to the Venue
Address:
21 Rue de Châteaudun, 75009 Paris, France
Building Access
DataDog - Café Versailles (Basement floor)
🪪 ID Requirements
bring your ID for check-in
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.
🌟 Inside Iceberg's Commit Path: Corruption, Latency, and Observability at Datadog
In this joint talk, three Datadog engineers explore what happens during an Apache Iceberg commit. We’ll dive into real cases of table corruption and rollbacks caused by network interruptions, how metadata size drives commit latency, and how Datadog monitors commit reports and observability tooling to keep Iceberg table commits healthy and predictable.
DataDog Duo Firas Omrane and Alessandro Nori
🌟 Apache Polaris (incubating), on-prem storage and more
After a quick update about Apache Polaris (incubating), we will see HMS and on-prem storage support.
JB Onofré is director of the ASF, PMC member on 20+ Apache projects.
🌟 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.
🌟 1000+ Columns and 5ms Latency: Building a Data Lakehouse for Flight Simulation with Apache Iceberg
The digital transformation of industry drives a crucial need for effective data management, especially for real-time applications. While many existing C++ solutions excel at monitoring and precisely tracking industrial activities, they struggle to historize data, hindering long-term analysis.
This talk presents a concrete experience report on how a leader in flight simulation overcame this challenge with Apache Iceberg. Faced with the complexity of digitizing a simulation process, this client had flight recordings, but lacked a digital representation of each simulation event.
We will detail the approach adopted to capture data from an existing C++ solution and integrate it with Apache Iceberg. This data, generated and captured in 5ms cycles, was integrated into Iceberg via a technical solution including the Apache Iceberg Sink Connector. The latter efficiently managed schemas with over 1000 columns, while ensuring real-time data capture and transmission, and data integrity.
This data capture and its restructuring for analytical purposes transforms flight simulation! New doors are opening: intelligent agents rely on this data, both in motion and at rest, to completely rethink the simulation exercise. They leverage both real-time and historical analysis to improve pilot training and optimize simulation execution.
Charly CLAIRMONT, has been campaigning since 2004 to advance open source and data-platforms both through his professional activity with Synaltic, and in community animation, such as the Hadoop User Group France, Paris Spark Meetup, Modern Data Stack France. He actively participates in the architecture of Data-Asso, the first directory of associations in France.
Notes
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