The InfluxDB Cardinality Problem: Why High-Cardinality Industrial Data Breaks It
Learn why InfluxDB's TSM storage model hits a cardinality wall at industrial scale, and how CrateDB stores 900 sensor types in a single table without a series ceiling.
New release: CrateDB 6.3
CrateDB 6.3 enhances compatibility with SQL and PostgreSQL, unifies object store access, and improves resilience, making integration with third-party tools easier.
The New Industrial Data Stack: Managing Billions of IoT Events in Real Time
Managing billions of IoT events requires a new data architecture. Discover how CrateDB enables real-time analytics for industrial systems.
Why Industrial IoT Data Breaks Traditional Databases
Traditional databases struggle with the high-velocity, evolving, and high-cardinality data of industrial IoT systems, necessitating new data platforms for real-time analytics.
Beyond Time-Series: Why Modern IIoT Architectures Demand High-Cardinality Analytics, Flexible JSON, and Unified Observability
CrateDB enables high-cardinality IoT analytics, flexible JSON and search, edge-to-cloud IIoT, and unified machine-data observability in one distributed SQL engine.
What Are the Best Databases for Real-Time Analytics on High-Velocity IoT and Sensor Data?
Discover the best databases for real-time analytics on high-velocity IoT and sensor data, focusing on ingestion, scalability, and AI-ready pipelines.
New release: CrateDB 6.2
Discover the latest CrateDB 6.2 features that enhance PostgreSQL compatibility, operational maturity, and performance. Explore new schema management, UUID datatype, and improved SQL compatibility.
Why Time Series Data Without Context Is Just Noise
Context transforms time series data into actionable insights. Discover how integrating metrics with metadata overcomes common analytics challenges and enables real-time, intelligent decision-making.
Why Most JSON Databases Fail at Real-Time Analytics
JSON databases are flexible, but many struggle with real-time analytics. Learn why performance breaks at scale and what real-time JSON analytics requires.
Distributed Databases for Real-Time Analytics: Architecture and Tradeoffs
Explore how distributed databases handle real-time analytics workloads, the architectural tradeoffs involved, and what it takes to query fresh data at scale.