Tiger Data Blog

The text provides an overview of the capabilities and use cases of TimescaleDB, highlighting its integration with PostgreSQL for hybrid search, which combines vector embeddings, BM25 keyword search, and temporal filtering to enhance search results in RAG applications. It suggests that PostgreSQL can replace multiple databases by incorporating extensions like BM25, vectors, JSONB, and time-series features, reducing the complexity associated with databases like Elasticsearch. The document also discusses optimizing TimescaleDB for manufacturing IoT by utilizing features such as hypertables and continuous aggregates to handle high-frequency sensor data. Various PostgreSQL extensions popular in 2026, such as TimescaleDB and pgvector, are mentioned, along with insights on deploying TimescaleDB vector search using the CloudNativePG Kubernetes Operator for AI time-series applications.