Bringing Vector Search to the Lakehouse with Apache Hudi

AI applications increasingly need to search data by meaning, not just by exact fields or keywords. Product descriptions, support tickets, documents, images, and other unstructured data are becoming first-class inputs to analytics and application workflows. But adding semantic search to lakehouse data has usually meant introducing a separate vector database, copying embeddings into it, and maintaining a sync pipeline to keep records, metadata, deletes, and updates aligned.