HiveMQ Enterprise Extension for PostgreSQL
About this Extension
With this extension, organizations can integrate MQTT data with PostgreSQL for storage and data analysis. The extension guarantees data security with TLS encryption and features a fully-customizable MQTT message template. Easily format MQTT data simply by using the ‘insert’ command.
Why use PostgreSQL for MQTT data?
PostgreSQL is a powerful open-source relational database management system known for its scalability, flexibility, and extensive feature set. Our extension allows seamless MQTT data integration with PostgreSQL and enables a reliable foundation for storing, analyzing, and deriving valuable insights from MQTT data.
Compatible with Timescale Time-series Database:
TimeScale is a leading open-source time-series database built on top of PostgreSQL, providing specialized capabilities for handling time-series data. The HiveMQ Enterprise Extension for PostgreSQL enables seamless integration of MQTT data with TimeScale. Efficiently store, process, and analyze time-stamped data and extract actionable insights to make data-driven decisions and enhance operational efficiency in energy, manufacturing, and more.
Compatible with CockroachDB Database:
The HiveMQ Enterprise Extension for PostgreSQL enables effortless integration of MQTT data with CockroachDB — an open-source distributed SQL database. It has the ability to handle large-scale deployments, provide strong consistency guarantees, and geo-replication capabilities make it suitable for storing and analyzing MQTT data in real-time IoT applications.
Here’s what is enabled by this extension in PostgreSQL:
Customize MQTT messages for PostgreSQL
- With the fully customizable templating system in this extension, you can define the exact format of MQTT messages that PostgreSQL ingests.
- Optimize the formatting for efficient data querying and quick analysis.
Real-time Data Analytics for Time-sensitive Responses
- Perform real-time analytics on incoming data and trigger automated alerts based on specific data conditions.
- For example, collect sensor data and automatically halt production when a critical parameter, such as temperature or pressure, exceeds predefined thresholds.
Asset Tracking and Management:
- Leverage PostgreSQL's geospatial capabilities to manage and analyze asset movements based on stored data.
- Implement efficient queries to retrieve real-time asset information.
Energy Management and Optimization
- Leverage PostgreSQL's time-series capabilities to analyze historical energy consumption data.
- For example, you can Identify peak demand periods, optimize power distribution, and implement load-balancing strategies to maximize energy efficiency and minimize costs.
Real-time Vehicle Diagnostics and Predictive Maintenance
- Real-time monitoring and analysis of vehicle diagnostics data for connected cars enables proactive maintenance and minimizes downtime.
- For example, a rental car company that operates a fleet of electric vehicles can store and analyze data from the vehicles (e.g. battery status, charging patterns, and energy consumption).
- With PostgreSQL, they can optimize charging schedules, monitor vehicle health in real time, and predict maintenance needs.
Try HiveMQ Enterprise Extension for PostgreSQL
Download the latest version of the HiveMQ platform, which contains an evaluation version of this extension, limited to a 5-hour operating window. You can reset this window by restarting the HiveMQ broker each time.
For the production version of this extension, please contact our sales team.
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.
Contact Sales
Interested in the PostgreSQL Extension or other HiveMQ products? Our sales team is here to help.
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