​Apache Iceberg™ Europe Meetup - live in London!

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​Livestream

​https://www.youtube.com/watch?v=HB631nE24a8

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​​Join us for the Apache Iceberg™ Europe Meetup in London! Our event is hosted in London co-hosted by Dremio, Fivetran 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

​🎙️ Andrew Madson - AI & Iceberg - What got us here won't get us there
🎙️ Anton Borisov & Nicoleta Lazar - StarRocks + Iceberg: Hot Queries over Cold Data
​​🎙️ Viktor Kessler - Safe Data Changes in the Lakehouse: WAP, Branching, and Tags in Apache Iceberg

​7:20 pm – Networking break

​🎙️ JB Onofré - Lightning Talk: Apache Software Foundation and Iceberg ecosystem
🎙️ Shubham Baldava - Reimagining data ingestion & compaction for Iceberg

​8:30 pm – More Networking
9:00 pm – Event close

​How to Get to the Venue

​Address:

​Barclays Innovation Hub Powered by Eagle Labs, 41 Luke St, London EC2A 4DP, UK

​Building Access

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​🪪 ID Requirements

​Bring an ID with you

​Presentations & Speakers

​​​​​​​​🌟 AI & Iceberg - What got us here won't get us there

​​​​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.

​​​​​​​​🌟 StarRocks + Iceberg: Hot Queries over Cold Data

​Apache Iceberg gives teams an open, flexible lakehouse foundation, but performance and cost still depend on how queries are served.

This talk explores how StarRocks works with Iceberg across several analytics patterns: direct reads, hybrid approaches that combine StarRocks-native data with Iceberg tables, and experimental incremental materialized view designs that use Iceberg snapshot changes to reduce refresh cost.

We’ll compare where each pattern fits, the trade-offs around freshness, latency, and efficiency, and share lessons learned from building and operating StarRocks-Iceberg architecture.

​​Anton Borisov is a Principal Data Architect who builds real-time data platforms at the edge of what's possible with Flink, Kafka, and emerging open-source technologies. At Fresha, he has led mission-critical projects ranging from zero-downtime Postgres migrations to streaming architectures powering real-time customer analytics. A recognized voice in the data streaming community, Anton writes deep technical explorations that influence how engineers approach Flink, Iceberg, Paimon and StarRocks, bridging research, open-source innovation, and production reality.

​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.

​​​​​​​​🌟 Lightning Talk: Apache Software Foundation and Iceberg ecosystem

​JB Onofré is director of the ASF, PMC member on 20+ Apache projects.

​​​​​​​​🌟 Safe Data Changes in the Lakehouse: WAP, Branching, and Tags in Apache Iceberg

​Modern data platforms increasingly behave like software systems — but most organizations still lack safe workflows for changing data. Updating a table in production can easily break downstream pipelines, dashboards, or machine learning models.

​In this session, we explore how Write-Audit-Publish (WAP) and Iceberg’s branching and tagging capabilities introduce Git-like workflows to the data lakehouse. Instead of writing directly to production tables, teams can validate changes, test transformations, and audit results before publishing them.

​We’ll walk through the core concepts behind WAP, branches, and tags in Apache Iceberg, and explain how they enable safer experimentation, reproducibility, and controlled data releases.

​To make this concrete, the session includes a live demo showing how to:

• ​write data to a staging branch

• ​validate and audit changes

• ​publish the result to production

• ​use tags to create reproducible data snapshots

​If you’re interested in bringing software engineering best practices to data management, this session will show how Iceberg makes it possible.

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​​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.

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​​​​​​​​🌟 Reimagining data ingestion & compaction for Iceberg

​Iceberg is meant for bigger scale and its not a traditional warehouse. Currently not many tools can handle ingestion in Iceberg with scale, speed and cost-efficiency from high-volume sources like databases, datawarehouses, Kafka or S3.

How can we achieve this :
- Load historical data parallely (create append files and commit linearly to avoid merge conflicts)
- Different DB techniques to chunk bigger table and achieve point 1.
- Golang & Arrow based ELT for lightweight and fast ingestion. (Arrow vs Iceberg-go vs Iceberg-java performance difference)
- Exactly once semantic via 2PC
- Paritioning, schema evolution
- File size optimisations
- Compactions (Conflict-free as well as subdivided between 3 stages - equality-to-positional, fragments to segments, full-compaction)

​Shubham Baldava 10+ years in Data Engineering | Ex: PayPay (Japan), ShareChat, Gameskraft (India)