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CrateDB for Industrial Time Series

The SQL database for complex, large scale time series workloads

CrateDB vs. InfluxDB Benchmark

Time series data processing is critically important to most IoT and sensor 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 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 to ingest and manage massive amounts of data from diverse sources. 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.

Extensible time series data models Elegant JSON handling and  Dynamic Schema. Automatically adapts schema to new JSON structures.

Time Series Data & SQL Interfaces
Built-in interfaces to Azure IoT Hub, Azure Event Hubs, Prometheus, 
Telegraf,and most any SQL tool via Postgres wire protocol - for example, Grafana -, JDBC, and REST.

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



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CrateDB Cloud

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.



Industrial Time Series at ALPLA

Manufacturer modernizes 180 factories with industrial times series data in CrateDB-powered IIoT platform.

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