Data Mesh is something every organization talks about but few are actually doing. This framework for federated data management and governance promises a lot and demands even more, but some elements in the paradigm can be beneficial for every organization: especially, data products & domains. Thinking in terms of products and domains also demands new approaches from data modeling – join this session to find out how data modeling works in the Data Mesh!

We are used to managing data before deploying AI: carefully collecting, cleaning and structuring it. But that is changing. AI now helps to improve data itself: automatically enriching, validating, integrating and documenting it. We are moving from static management to dynamic improvement: AI brings data to life and changes how we deal with it.

The data lake world is shifting from Hive to formats like Iceberg, Hudi, Delta Lake and DuckLake. This session offers practical guidance on schema evolution, time travel, ACID and metadata, highlighting pros, pitfalls and costs so you can choose the right format with confidence.

A five-week Data Governance Sprint uses a structured, workshop-driven method to cut debate, align stakeholders, and deliver practical results fast. It establishes clear roles, a business glossary, an operating model, and early wins that build momentum for data governance.

Organizations face complex data challenges that common architectures—data warehouses, lakes, lakehouses, and fabrics—only partly solve. A holistic architecture covering the full data journey, from source to insight, is needed, with these architectures serving as components of a larger whole.