Learn the three SQL patterns that power predictive maintenance: threshold triggers, trend detection, and cross-asset correlation. See how CrateDB serves as the data layer under ML models for industrial IoT at scale.
Learn how to seamlessly ingest OPC-UA and MQTT data into SQL using Telegraf and CrateDB, enabling real-time insights and efficient data management for industrial IoT.
Learn why industrial analytics become siloed per facility, why consolidation layers fail at operational speed, and how manufacturing and logistics teams query all sites in one SQL statement.
Learn why cloud-only analytics fails at the factory floor and how CrateDB's edge deployment handles OT/IT separation, DACH data sovereignty requirements, and sub-second query latency on live sensor data.
Build real-time industrial IoT analytics with Telegraf, CrateDB, and Grafana. SQL examples, architecture patterns, and proof from ALPLA, ABB, and TGW. Learn more.
Learn why batch export architectures make OEE dashboards too slow for shift supervisors, and how manufacturing teams cut query times from 3 to 5 minutes to milliseconds with a real-time analytics database
In this post, I am going to show you how to set up Telegraf and have it send metrics data to CrateDB.
Compare data historians and time series databases on OT connectivity, query language, and analytics depth. Learn why most industrial stacks benefit from both.
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.
CrateDB 6.3 enhances compatibility with SQL and PostgreSQL, unifies object store access, and improves resilience, making integration with third-party tools easier.
