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# OLTP in the Lakehouse: Redefining Data for AI Workloads


- **Event:** World Congress 2026 Europe - Virtual Stage

## Description

Modern AI workloads increasingly demand instant access to up-to-date transactional and analytical data, yet traditional architectures remain siloed, enforcing unnecessary data pipelines. Databricks Lakebase, a new extension to the Lakehouse architecture, brings OLTP capabilities to the platform that already drives ELT, BI, and machine learning. In this talk, we will break down how Lakebase helps create a unified storage layer to enable real-time decisioning, adaptive AI systems, and streaming analytics directly in the Lakehouse. We will unpack the architectural foundations of Lakebase and demonstrate how unifying OLTP and analytics redefines the data stack for AI. 

## Speaker

### [Oleksandra Bovkun](https://www.wearedevelopers.com/@oleksandra-bovkun)

Developer Advocate at Databricks 


## Related talks at this congress

- [Building an AI-Ready Content Lake: Scaling RAG and Document AI Beyond Demos](https://www.wearedevelopers.com/events/world-congress-2026-europe-virtual-stage/sessions/1506-building-an-ai-ready) — Angel Borroy
- [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/events/world-congress-2026-europe-virtual-stage/sessions/1498-beyond-dashboards) — Piotr Menclewicz
- [Why Your AI Agent Keeps Hallucinating Your Data: Building Deterministic Context Layers](https://www.wearedevelopers.com/events/world-congress-2026-europe-virtual-stage/sessions/1651-why-your-ai-agent) — Tom Kaltofen, Xiaoheng Chen
- [From Data Mesh to AI Mesh: Integrating Distributed Intelligence on Decentralized Data Architectures](https://www.wearedevelopers.com/events/world-congress-2026-europe-virtual-stage/sessions/1542-from-data-mesh-to-ai) — Olga Woschitz, Thomas Görz