
Budapest Databricks Meetup #3: Metadata, AI, and SAP Integration in Practice
The next Budapest Databricks Meetup, organized by Abylon, will take place on May 27. Once again, we’re bringing together Budapest’s data and AI community for an evening of knowledge sharing, practical insights, and professional networking.
The third meetup will focus on the role of metadata in enterprise AI, the practical application of end-to-end data lineage, and the integration of SAP and Databricks systems. Drawing on real-world project experience, the event will demonstrate how to build more transparent, intelligent, and better-integrated data platforms.
Participants will gain practical insights from experts who work with Databricks environments and modern enterprise data platforms every day. Topics will include how metadata can support next-generation AI solutions, how lineage-based approaches can help identify the causes of KPI changes more quickly, and how to efficiently integrate SAP data into Databricks platforms.
Who is this Databricks Meetup for?
The event is particularly relevant for data engineers, data analysts, BI professionals, and AI experts, but it will also appeal to anyone interested in modern data platforms and the Databricks ecosystem.
If you work with Databricks, build enterprise data platforms, or want to deepen your understanding of SAP and AI, join us! After the presentations, there will also be plenty of opportunities for networking and professional discussions.
Our Speakers
- Miguel Angel de Luna Gomez (Practice Lead EMEA, Databricks) – xploring how metadata is transforming enterprise AI solutions and how Databricks is helping shape the future of intelligent data platforms.
- Vedelek Edina & Vetési Olivér (Abylon Consulting) – Exploring end-to-end lineage approaches across Databricks and Power BI environments to provide greater transparency into KPI changes and report dependencies.
- Takács Gábor (Abylon Consulting) – A practical look at integrating SAP data into Databricks environments, including common challenges and a data engineering perspective.


