HiveMQ Enterprise Integration for Timescale

Category: Data Integration
Version: Bundled with HiveMQ   |   License: Commercial
Provider: HiveMQ    |   Verified: yes

Product Resources
Please use the following links to download and try the extension, read the installation guide, learn more about features, or find out how we can help.

About the Integration with Timescale

The HiveMQ Enterprise Extension for PostgreSQL enables effortless integration of MQTT data with the TimeScale database, designed to handle large volumes of time-stamped data efficiently. With this extension, you can efficiently store, process, and analyze time-stamped data, extract actionable insights to make data-driven decisions and enhance operational efficiency in energy, manufacturing, and other industries. This integration is secured by TLS.

Why use Timescale for MQTT data in IoT environments?

Timescale is specifically created and optimized to handle time-series data. Through partitioning and compression techniques, it can efficiently store large volumes of timestamped data generated from IoT devices, such as sensor readings, telemetry, and event data. Timescale also enables fast and flexible analytics tasks on MQTT data, scales horizontally to accommodate growing IoT deployments, and benefits from PostgreSQL’s extensive ecosystem of tools and libraries.

Here’s what the HiveMQ PostgreSQL extension for data integration with Timescale enables for IoT:

Utilize hyper tables to partition time-based data and reduce disk space usage automatically:

  • This technique helps efficiently store time-series data and optimizes query performance and scalability.
  • It also minimizes disk space requirements.
Reduce disk space usage automatically
Aggregation and IoT Analytics

Aggregation and IoT Analytics

  • Perform various analytics tasks on MQTT data, such as data aggregation over time intervals, computing statistics, detecting anomalies, or generating insights.
  • Use Timescale’s advanced time-based indexing and continuous aggregations for fast and flexible analytical queries on time-series data.
  • Timescale can be utilized in a smart energy monitoring system to detect anomalies in energy consumption. By employing statistical algorithms, it can identify deviations from expected behavior, enabling the detection of energy wastage, equipment malfunctions, and potential energy-saving opportunities.

Leverage the scalability features of PostgreSQL

  • As MQTT data volume increases, Timescale leverages the scalability features of PostgreSQL, including parallel query execution and distributed architectures, to handle large-scale time-series workloads.
  • It can also scale horizontally across multiple machines to accommodate growing data requirements.
Scalability features of PostgreSQL

Need help?

We’re always happy to answer any questions about the HiveMQ Enterprise Extension for PostgreSQL use cases or the installation process. Reach out to our support team.

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