Uploaded May 2026 | Updated September 2026, 2 weeks ago
Search engine for keywords. Vector DB for semantics. Cache for filters. Glue code to merge it all. Sound familiar?
Most production search stacks are Frankenstein stacks—three separate systems because no single layer handled everything users actually do: filter by keywords, match on semantic meaning, analyze results, sort by rating. Until now.
🔹 Collapse full-text, #VectorSearch, filters & aggregations into Amazon ElastiCache for Valkey
🔹 Watch a live build of a hybrid query that blends keyword precision with semantic understanding in one call
🔹 See how facets & counts work without standing up a separate search cluster—critical for #Ecommerce catalogs
If your search architecture has more moving parts than your actual product, this breakdown shows you how to simplify without losing relevance. #Database
Search engine for keywords. Vector DB for semantics. Cache for filters. Glue code to merge it all. Sound familiar?
Most production search stacks are Frankenstein stacks—three separate systems because no single layer handled everything users actually do: filter by keywords, match on semantic meaning, analyze results, sort by rating. Until now.
🔹 Collapse full-text, #VectorSearch, filters & aggregations into Amazon ElastiCache for Valkey
🔹 Watch a live build of a hybrid query that blends keyword precision with semantic understanding in one call
🔹 See how facets & counts work without standing up a separate search cluster—critical for #Ecommerce catalogs
If your search architecture has more moving parts than your actual product, this breakdown shows you how to simplify without losing relevance. #Database
