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Abstract cover artwork for Vector Databases and Semantic Search at Scale, a Data Engineering course Data Engineering
Data Engineering intermediate 10 hours 4.8

Vector Databases and Semantic Search at Scale

Taught by Daniel Okafor

How approximate nearest-neighbour indexes actually work (HNSW, IVF, product quantisation) and what you trade for speed. Metadata filtering, hybrid dense plus sparse retrieval, reciprocal rank fusion, re-indexing strategies for embedding migrations, and keeping a vector store in sync with a relational system of record.

What you'll cover

qdrant vector database embeddings hnsw hybrid search

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