The SQL database for complex, large scale time series workloads in industrial IoT.
Time series data processing is critically important to most IoT and machine data use cases.
Industrial IoT presents an unusually challenging time series data use case. It is orders of magnitude larger in scale and complexity than other time series workloads, such as those found in IT systems monitoring.
Industrial Time Series Data in Smart Factories
Connected factories, energy networks, smart city infrastructure, and vehicle fleets generate a massive amount of complex time series data. Here are some real-life stats from manufacturing companies Crate.io works with:
- Data flowing from 100+ factories and 1000+ production lines
- 1,000 different sensor message data structures
- 900,000 inserts per day
- 100+ terabytes of retained data
- Real-time dashboards executing ~ 1 million queries per day
If your time series data requirements look similar, then time series databases (TSDBs) like InfluxDB, and Amazon Timestream may not be able to cope. They are built for smaller time series data use cases like IT monitoring.
Using CrateDB for Industrial Time Series Data
CrateDB is built for industrial time series data. As an ANSI SQL DBMS, CrateDB is easy to learn, easy to integrate, and does not lock you into proprietary data access interfaces as so many TSDBs do. Here are some reasons people choose CrateDB to manage industrial time series data:
Real-time query performance
Partitioning, parallel processing and in-memory columnar indexes enable real-time, complex analytics & AI on time series data, not just simple aggregates.
Millions of data points per second
Distributed processing, data partitioning, and multi-threaded architecture enable fast, linearly scalable time series data ingestion.
Scales without limits
Elastic scaling, partitioning, sharding, & replication for fast, always-on performance that stays consistent as data volume and concurrent clients grow.
Built-in high availability
Automatic replication & self-healing for non-stop performance
CrateDB vs. Time Series DBs
In a benchmark querying 314 million rows of sensor readings, CrateDB executed 10x more queries per second under load than InfluxDB.
Industrial Time Series Data Resources
Designed to handle the complexity of high-end
time series workloads in real-time.
Time Series DB Design Techniques
Distributed SQL best practices for fast, scalable, always-on time series.