One signal

A new architecture guide examines running vector search directly over Apache Iceberg tables instead of maintaining a separate vector database for retrieval-augmented generation systems.

Why it matters

The practical attraction is fewer copies of the same data, which may simplify synchronisation, deletions and governance. The trade-off is that exact search over lakehouse data will not suit every scale or latency target. The guide recommends treating a dedicated vector index as a derived cache where the workload demands it, rather than assuming every AI system needs one by default. A useful antidote to buying infrastructure first and asking questions later.

Discuss: For production RAG, would you prioritise a simpler single data store or the speed and flexibility of a dedicated vector index?

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