Discover how CrateDB excels in high-cardinality time series analytics, offering real-time, flexible, and scalable SQL-based solutions for modern data challenges.

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

High cardinality in time series databases refers to handling millions or billions of unique values across dimensions like device IDs, user IDs, and tenant IDs. Traditional time series databases struggle with high cardinality because they were designed for fixed metrics and predictable queries, not event-driven analytics with dynamic dimensions. CrateDB addresses this by offering SQL-based analytics without pre-aggregation, supporting real-time queries on raw events, distributing data automatically across nodes, and maintaining predictable performance as cardinality grows. The system is particularly suited for IoT telemetry, multi-tenant SaaS analytics, and operational intelligence where dimensions grow continuously and queries are exploratory rather than predefined.

High Cardinality Database for Real-Time Analytics

Why High Cardinality Breaks Traditional Time Series Databases

Why "Unlimited Cardinality" Is the Wrong Promise

Dimensions Are the Hard Part of Time Series Analytics

CrateDB’s Unique Strengths for High-Cardinality Time Series

Real-World Use Cases That Depend on High Cardinality