Integrated policy and data transformation engine that validates, enforces, and manipulates data in motion to ensure data integrity and quality across your MQTT deployment.
Organizations lose millions annually due to poor data quality (Gartner)
3% Lower Revenue
Bad data costs enterprises 30% of their revenues (Ovum Research)
5% Data Mistrust
Over two-thirds of C-suite execs don’t trust the data coming from their systems (Deloitte)
Why IoT Stream Governance?
With the rapid growth of IoT and real-time data, the need for trusted, high-quality data across systems is becoming essential. While tools for managing IoT connections and ensuring basic data flow exist, guaranteeing that data is accurate, reliable, and actionable across teams has been a persistent challenge. This is especially true for businesses deploying distributed IoT ecosystems supported by diverse devices and complex infrastructures.
HiveMQ Data Hub Key Benefits
Maximize the business value of data being transported by defining data policies, transformations, and validation requirements to ensure the data is accurate and meets the standards your organization requires.
Provide faster business insights on validated data - stop acting on rogue data and generating more noise than insights.
Increased Data Quality
Ensure data quality standards are centrally defined and enforced across all devices and messages.
Operational Efficiency
Stop bad-acting devices from misusing MQTT connections, sending bad data, and monopolizing resources.
Reduced Costs
Reduce redundant processing and storage costs by only acting on good data. Reduce the impact of acting on bad data.
Improve Data Integrity
Quarantine and further investigate bad data to prevent it from contaminating your systems and ultimately ensuring your data is accurate and reliable.
Data Management
Manage everything in a single system, allowing data to be processed faster and negating the need to manage another standalone system to ensure data quality.
HiveMQ Data Hub allows us to automatically validate and standardize data to build a more reliable data pipeline and further grow our business
HiveMQ Data Hub provides mechanisms to define how MQTT data is handled in the HiveMQ broker. This ensures that data quality is assessed at an early stage in the data supply chain, eliminating the need for subscribers to perform resource-intense validation before data reaches downstream devices or upstream services.
Key Data Hub Components
Policy Engine
Ensure high data integrity by setting and enforcing rules that govern how the entire system operates, controlling what data is allowed in and what is filtered out.
Transformers
Guarantee trusted data by applying transformation rules that standardize, clean, and enrich data, ensuring it meets required standards and structure.
Industry Modules
Faster time to solution using industry specific no-code modules like SparkPlug and incorporate business-specific modules without complex configurations.
HiveMQ Data Hub Capabilities
The HiveMQ Data Hub provides the following capabilities to help you enforce data integrity and quality.
Enforce Behavior Policies
Define behavior policies to manage device interactions with the MQTT broker, including logging, stopping, or transforming actions. Flow-control validates message flow patterns, while the scripting engine enables custom behaviors using JavaScript functions.
Transform Data
Convert or manipulate data formats as it moves through the MQTT broker before reaching consumers. These data transformations enable more processing at the edge, streamlining data integration. For example, convert Fahrenheit to Celsius.
Define Data Policies
Set structured blueprints for data formats and enforce specific rules for incoming messages. Support for JSON and Protobuf formats ensures data integrity, with options to specify requirements, like temperature readings within defined ranges.
Simplify Workflow via Modules
Use specialized Data Hub modules that simplify data transformation and policy enforcement without coding. These modules bundle schemas, scripts, and policies into ready-to-use solutions for real-time automation.
Visualize Data
Utilize the simple user interface to manage schemas, data, and behavioral policies. The dashboard provides an overview of overall quality metrics, making it easy to locate bad actors and bad data sources. Visualize the MQTT data further in tools like Grafana.
Interact with RestAPI
Use HiveMQ’s REST API for programmatic interactions with the HiveMQ Enterprise MQTT broker, enabling seamless integration with other applications. The API supports sending and receiving data as JSON objects.
Data Schema
Create the schema of how data should be formatted. Both JSON and Protobuf formats are currently supported. These can vary from simple to complex. The example on the right is a schema that validates GPS coordinates.
An appropriate policy tells HiveMQ how to handle incoming MQTT messages to enforce the rules and structure that the schema outlines. The example on the right drops the message and logs the result, but these can be arbitrarily complex and could re-queue the message. On the re-queue, the system could also apply transformation functions to fix bad data.
A look inside the HiveMQ Control Center which hosts a library of modules which can help quickly implement functionality like fanning out Sparkplug metrics, or dropping duplicate messages. The modules also helps easily transform data without requiring custom code or complex configuration. This is a growing collection with the potential of customers adding their own modules.
Visualization
A demo use case introduces a quality metric and visualizes it in a Grafana dashboard. An addition to the quality metric is the list of bad clients queried from a PostgreSQL database. A screenshot of the dashboard includes the data quality on the left hand side and list of the top 10 bad clients on the right hand side.
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