TimescaleDB

Scalable Time-Series Database Built on PostgreSQL

TimescaleDB is an open-source time-series database that combines the reliability and advanced SQL features of PostgreSQL with optimizations for high-volume time-series workloads. Perfectly suited for industrial automation, IoT, and telemetry applications, TimescaleDB enables the storage, querying, and analysis of millions of datapoints—while maintaining full SQL compatibility.

Whether you’re managing sensor data, process logs, or industrial KPIs, TimescaleDB gives engineers and developers the performance and flexibility they need without compromising on data integrity or tooling.

Key Features

• Full PostgreSQL Compatibility: Use standard SQL and PostgreSQL tooling for seamless integration.

• Time-Series Optimizations: Automatic partitioning, compression, and continuous aggregates for high performance.

• Scalable & Reliable: Handles large datasets with linear scaling and proven PostgreSQL stability.

• Built-in Analytics: Ideal for forecasting, anomaly detection, and aggregating data over time.

• Flexible Data Model: Supports relational and time-series data in the same database.

• Open Source with Enterprise Extensions: Extendable via Timescale’s advanced features and APIs.

Ideal Use Cases

• Storing and querying high-resolution machine data or telemetry.

• Building digital twins with historical and real-time data streams.

• Managing time-series logs from edge devices or factory systems.

• Performing SQL-based analytics for condition monitoring and reporting.

FLECS Benefits

Install TimescaleDB directly from the FLECS Marketplace to deploy a production-ready time-series engine on your Linux-based industrial controller. Combine it with visualization tools like Chronograf or Grafana and deploy custom queries or dashboards—all from the FLECS platform.

Instructions

When installing TimescaleDB, we set "root" as the initial password for the Postgres user.
Please log in with an appropriate SQL client after installation and change the password immediately.