Query your Data Lake with Natural Language

Extracting insights from structured data in enterprise data lakes has traditionally required analysts to search catalogs, write SQL queries, and translate results — a slow, resource-intensive process limiting data accessibility. This webcast explores how natural language querying automates this workflow using generative AI and vector search. Topics include: How vector databases enable semantic search across catalog metadata How large language models generate and execute SQL from plain-language questions How an AI pipeline delivers human-readable answers without SQL expertise Watch the full webcast to explore this approach in depth.

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