​Apache Iceberg™ Europe Meetup - live in Barcelona!

​Join us for the very first Apache Iceberg™ Europe Meetup in Barcelona! Our event is co-hosted by Kantox 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 (Vakamo)- Apache Iceberg - Governance and Business Continuity 
🎙️Yohan Valencia (Kantox)- Who Gets to See What: Governance in Our Open Lakehouse

​🎙️ Miguel Sotomayor Glassnode - One Lake, Three Consumers: Apache Iceberg as the Unified Lakehouse Layer for Snowflake, BigQuery and a Real-Time API
🎙️Tyler Hannan (Clickhouse) Lightning Talk

​🎙️ Olena Kutsenko (Confluent) The journey of a record from Kafka topic to analytics table

​8:15 pm – More Networking
9:00 pm – Event close

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​Livestream

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​How to Get to the Venue

​Kantox 21 st floor

​🪪 ID Requirements

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​Presentations & Speakers

​​​​​​​​​​​🌟 Apache Iceberg - Governance and Business Continuity 

​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.

​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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​🌟 Who Gets to See What: Governance in Our Open Lakehouse

​Every time a new client joins your platform, someone has to write a grant, open a ticket, or update a policy. At Kantox we decided that should not be the case. Starting from a real business project, we evolved our Iceberg lakehouse to support Kafka streaming, adopted Lakekeeper as our REST catalog, and built a full governance layer on top using OPA for row and column access control and ODCS contracts as the source of truth. In this session you will see how all the pieces connect, and a live demo showing access rules changing in real time driven entirely by identity.

​Yohan is a Senior Data Platform Engineer with 8+ years of experience specializing in distributed data platforms and open lakehouse architectures. I work daily with Apache Iceberg, Kafka, Trino, Lakekeeper, OPA, and ODCS, building event-driven and analytics infrastructures from the ground up across AWS, GCP, and Azure. I have hands-on experience with data governance, GDPR compliance, and fine-grained access control, and I am passionate about balancing pragmatic stability with the thoughtful adoption of emerging data technologies and have contributed to the Lakekeeper open-source project

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​​🌟 One Lake, Three Consumers: Apache Iceberg as the Unified Lakehouse Layer for Snowflake, BigQuery and a Real-Time API

​What if a single Iceberg table on GCS could power your data warehouse queries and your production API? At Glassnode, the leading provider of on-chain and financial metrics for digital assets, we deal with high-frequency time series data spanning hundreds of blockchain metrics across multiple cryptocurrencies. Serving that data reliably across three very different consumers — without duplicating pipelines or storage is the core challenge. The answer was a lakehouse architecture with Apache Iceberg on GCS as the single source of truth for all three: Snowflake, BigQuery, and a REST API serving real-time requests. In this talk we'll share how we built it and what we learned along the way: Why the lakehouse model and Iceberg as an open table format make this architecture possible How Snowflake and BigQuery consume the same Iceberg tables natively How a REST API reads directly from Iceberg on GCS for real-time serving The specific challenges of high-frequency time series data: snapshot management, compaction, and latency What works well today and where you still need workarounds A talk about a real production use case, for data engineers wondering whether Iceberg can be more than just a batch analytics tool.

​Miguel Sotomayor is a Senior Data Engineer at Glassnode with 15+ years in big data. He works daily with Apache Iceberg, Spark, Snowflake and Google Cloud, and teaches Apache Spark in the Master Big Data Analytics programme at Datahack School.

​​🌟 The journey of a record from Kafka topic to analytics table

​Modern data platforms increasingly rely on streaming systems like Apache Kafka as the source of truth—but turning event streams into reliable, queryable analytics tables is far from trivial.

​This talk explores what really has to happen when moving data from a Kafka topic into an Apache Iceberg table. Beyond simple ingestion, we will walk through the essential steps: schema evolution, data normalization, partitioning, exactly-once guarantees, late-arriving events, and table maintenance.

​Using Confluent Tableflow as one concrete example, we will examine how these challenges can be addressed in practice. Along the way, we will compare alternative approaches—such as Kafka Connect sinks, stream processing pipelines, and custom ingestion frameworks—to highlight trade-offs in correctness, latency, and operational complexity.

​The goal is not to promote a single solution, but to provide a mental model for designing robust streaming-to-lakehouse pipelines, helping you understand what matters regardless of the tooling you choose.

Olena Kutsenko is a Staff Developer Advocate at Confluent and a recognized expert in data streaming and analytics. With two decades of experience in software engineering, she has built mission-critical applications, led high-performing teams, and driven large-scale technology adoption at industry leaders like Nokia, HERE Technologies, AWS, and Aiven.

​A passionate advocate for real-time data processing and AI-driven applications, Olena empowers developers and organizations to use the power of streaming data. She is an AWS Community Builder, a dedicated mentor, and a volunteer instructor at a nonprofit tech school, helping to shape the next generation of engineers.

​As an international speaker and thought leader, Olena regularly presents at top global conferences, sharing deep technical insights and hands-on expertise. Whether through her talks, workshops, or content, she is committed to making complex technologies accessible and inspiring innovation in the developer community.