Apache Iceberg™ Europe Community Meetup - Munich

Apache Iceberg™ Europe Meetup - Munich!

Join us for the Apache Iceberg™ Europe Meetup! Our event is hosted in Munich co-hosted by Starburst, SVA 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.

Livestream

https://www.linkedin.com/events/7470866611726565378/

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 (Vakamo)- Apache Iceberg - Governance and Business Continuity with Lakekeeper

🎙️ Marius Grama (Starburst) - Designing a Zero-Trust Lakehouse: Token Passthrough, Credential Vending and Server-Side Scan Planning in Apache Iceberg

🎙️ Alexander Köhler (Finanz Informatik) and Christian Bandowski (SVA) - Governance at Scale: Building an Apache Iceberg Data Lakehouse for Hundreds of Savings Banks

7:20 pm – Networking break

🎙️ Can Artuc - Below the Waterline: Ice Cold Operations in the Iceberg Ecosystem

🎙️ Oliver Hänsel ( NetApp ) - Efficient migration from Hadoop Hive to Iceberg on object storage

8:15 pm – More Networking

9:00 pm – Event close

Building Access

SVA System Vertrieb Alexander GmbH

Bothestraße 13

81675 München

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.

🌟 Designing a Zero-Trust Lakehouse: Token Passthrough, Credential Vending and Server-Side Scan Planning in Apache Iceberg

Securely scaling multi-user lakehouses with Starburst and Trino requires moving beyond static, shared service accounts.

This session demonstrates how implementing OAuth 2.0 Token Passthrough forwards individual user identities directly to  Iceberg REST catalogs like

Lakekeeper and Unity Catalog. Once authenticated, the Iceberg REST Catalog makes use of dynamic credential vending to issue path-scoped, short-lived

storage tokens across AWS, Azure, and GCP, automatically refreshing them mid-query to prevent long-running job failures. Finally, we explore how

Server-Side Scan Planning offloads metadata processing evaluation to the Iceberg REST Catalog Server, enforcing centralized fine-grained

governance before any data access is granted.

Marius Grama is a software engineer collaborating with Starburst Data and an avid Trino contributor, specializing in distributed query engines and open table formats like Apache Iceberg for the past five years of this decade. His work focuses on optimizing Trino, a high-performance engine that completely decouples compute from storage to eliminate unnecessary data movement. By enabling organizations to run ANSI SQL queries directly where their data lives without expensive ETL pipelines, Trino serves as the essential backbone for scaling modern, secure open data lakehouses.

🌟 Governance at Scale: Building an Apache Iceberg Data Lakehouse for Hundreds of Savings Banks

Session Abstract

Finanz Informatik is continuously evolving its Data Analytics Platform into the central AI and data platform for Germany’s Savings Banks Finance Group (Sparkassen-Finanzgruppe). The journey began with initial analytics initiatives in 2020 and has since led to a modern data lakehouse architecture built on open standards such as Apache Iceberg.

In this session, we provide practical insights into the architecture and lessons learned from building a platform that enables hundreds of institutions to access thousands of data assets. The focus is on the governance requirements of a highly regulated environment and how data access, tenant isolation, and authorization can be managed efficiently and centrally.

We will demonstrate how Starburst, Apache Iceberg, and Lakekeeper work together, discuss the technical and organizational challenges encountered along the way, and explain why we adopted a centralized governance architecture based on Cedar policies and OPA integration. In addition, we will share our experience with implementing transparent cost allocation in an enterprise-wide data lakehouse environment.

Attendees will gain real-world insights, architectural guidance, key design decisions, and practical lessons learned from operating a large-scale production platform.

Alexander Köhler is a Project Lead for AI and Data Platforms at Finanz Informatik, the central IT service provider for Germany’s Savings Banks Finance Group (Sparkassen-Finanzgruppe). Since 2020, he has helped build and evolve the organization’s analytics and AI platforms, first as a DevOps/MLOps engineer and later in platform-wide leadership roles. Today, he leads strategic AI and data initiatives and is responsible for driving the evolution of the underlying platform capabilities. Within the Data Lakehouse program, he focuses on the platform architecture and governance foundations that support data and AI workloads at scale.

Christian Bandowski is a Team Lead and Big Data Architect at SVA with a focus on modern Data Lakehouse architectures and platform engineering. He supports organizations in building scalable data platforms across cloud, on-premises, and hybrid environments, helping them establish the foundations for data-driven and AI-powered solutions. With a background in software development and architecture, he applies software engineering best practices to data platforms, governance, and data engineering challenges. He is also actively involved in evaluating new technologies and establishing strategic partnerships in the Data Lakehouse ecosystem. His work focuses on delivering robust and governed platforms that can scale across teams, use cases, and business domains.

🌟 Below the Waterline: Ice Cold Operations in the Iceberg Ecosystem

We migrated to Apache Iceberg three years ago. The architecture evaluation was thorough: partition evolution, schema evolution, CDC, time travel, multi-engine compatibility. Query performance improved. Costs dropped.

Six weeks later, the first governance incident arrived. The warehouse had one access-control plane. Iceberg on object storage gave us three. One drifted. We caught it later than we should have.

This talk introduces "Architecture Fitness vs Operational Readiness" as two distinct evaluations every Iceberg migration must run. Using our production experience and the 2026 ecosystem reality (catalog wars, streaming compaction challenges, managed vs self-managed tradeoffs), I'll show why most migration decisions only do the first evaluation, and provide a concrete operational readiness checklist for the second.

You'll leave with 5 questions to ask before your next platform decision, and a framework to distinguish architecture proposals from migration plans.

Can is Data Platform Architect with 17 years of experience transforming complex technical landscapes into simple, scalable data architectures. Architected and delivered 14 General Data Protection Regulation (GDPR), ISO 27001, and ISO 13485 compliant data platforms across telco, digital health, media, conversational AI, and deep-tech imaging by serving 100M+ users across continent-scale distributed systems processing 2.5+ PB of data. Designed and implemented 4 data mesh architectures, reducing infrastructure costs by 35% while accelerating time-to-market 3-4x, driving data architecture strategy across programs with 3,000+ engineers. From startup founding to enterprise transformation, I specialize in building architectural practices that connect AI, hardware, software, data, and cloud engineering into production-grade systems that scale from prototype to global deployment with 99.95% uptime. I've worked for Ericsson, Sky, Telefonica and startups/scale-ups.

🌟 Efficient migration from Hadoop Hive to Iceberg on object storage

Legacy HDFS-Hive data architectures are currently undergoing modernization through migration to Apache Iceberg solutions built on object storage. While traditional Hive environments are frequently constrained by manual management and a lack of ACID transactions, Iceberg stands out with automated partitioning, enhanced resilience, and significantly faster query performance. Furthermore, it is crucial to closely evaluate the underlying storage platform and architecture during this transition. Optimizing at the storage layer not only accelerates the migration itself but also ensures seamless data access and a future-proof platform that is scalable, integrated, and highly performant.

Oliver Hänsel is a Technology Expert at NetApp, specializing in modern object storag