A time series database manages data indexed by time, enabling fast analysis of streaming data. Discover how CrateDB combines SQL simplicity, unlimited cardinality, and context enrichment to deliver real-time insights at scale.

CrateDB offers insights into distributed SQL database, IoT data management, and real-time analytics, providing documentation, tutorials, and best practices for deploying and scaling CrateDB in production environments. By exploring CrateDB's curated content, developers can learn about distributed query execution, data sharding, and replication strategies for building scalable and resilient database solutions. Whether you're collecting sensor data, analyzing log streams, or processing time-series events, CrateDB offers resources to handle high-volume, high-velocity data workloads and unlock the potential of distributed SQL databases.

CrateDB

Time series databases manage data indexed by time for real-time analytics at scale. CrateDB differentiates itself by combining PostgreSQL-compatible SQL with unlimited cardinality support, enabling context-enriched analytics that blend fast-moving telemetry with metadata in a single query. Unlike traditional TSDBs that separate metrics from context, CrateDB uses distributed columnar storage to handle billions of unique series while supporting mixed data types, real-time joins, and horizontal scalability without performance degradation.

What Is a Time Series Database (and Why CrateDB Goes Beyond It)

The Real Challenge: Time Series Without Context Is Just Noise

CrateDB: More Than a Time Series Database