Energy companies trust Timescale.
increase in application speed
of sensor readings daily
requests per day
Collect data using protocols like Modbus or OPC UA, stream it through MQTT, Kafka, or Kinesis, or manage ingest with tools like HighByte, UMH, or Litmus Edge. Timescale lets you store time-series data and relational context—such as MES records or asset metadata—in a single, unified database. However your pipeline is built, Timescale delivers fast analytics without lock-in, all using standard SQL.
Escape the Multi-Database Trap.
Get a single Postgres database that can store and join both high-cardinality time-series metrics from your sensors, and relational metadata—seamlessly integrated with Grafana, Tableau, and your existing MES/SCADA systems.
Avoid storing tag metadata with every sensor reading.
Separate tag metadata from time-series data to reduce storage, improve ingest performance, and allow updates—like renaming sensors or fixing units—without rewriting historical data.
Skip the Learning Curve.
Use standard Postgres and SQL—no proprietary query languages, no vendor lock-in, and no need to learn yet another custom interface just to access your data.
Query Optimization Without Trade-Offs.
Automatically partition time-series data and store it in a compressed columnar format—reducing storage by up to 97% while maintaining millisecond query performance, even at scale.For even faster results, use to incrementally precompute and materialize query results—reducing load while keeping queries fast and fresh.
Top-rated support
You want support to ensure guaranteed uptime for your production environment. We are available 24 x 7 x 365 to provide that support when unexpected events happen.
Learn how Timescale can support your smart factory in production by putting it to the test.