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Fast distributed SQL query engine for big data analytics.

The Industrial Data Platform for Agentic AI, connecting real-time operational data with MQTT for trusted, AI-ready pipelines.

The HiveMQ MQTT Platform is a purpose-built industrial data platform designed to bring operational data from the edge into the enterprise. It leverages the MQTT protocol to reliably stream real-time data across devices, systems, and applications. The platform focuses on connecting, contextualizing, analyzing, and acting on real-time and historical data, enabling AI-driven operations. Key features include a resilient real-time data backbone, unified namespace for aligning OT and IT, and the delivery of governed, contextualized data for AI consumption. HiveMQ allows users to build resilient, real-time data pipelines for mission-critical operations, feeding AI with trusted data and driving continuous improvement through automated decisions.
The HiveMQ MQTT Platform is a purpose-built industrial data platform designed to bring operational data from the edge into the enterprise.
Explore all tools that specialize in data analysis. This domain focus ensures HiveMQ MQTT Platform delivers optimized results for this specific requirement.
A centralized, governed data model that provides a single source of truth for all operational data, enabling consistent contextualization and access for OT and IT systems.
Real-time data processing capabilities that allow users to transform and enrich data as it streams, adding metadata, performing calculations, and filtering irrelevant information.
Leverages intelligent agents to automatically validate data quality, trigger alerts, and execute actions based on predefined rules and conditions.
Provides a semantic layer that abstracts the underlying data sources and enables users to query data using a standardized language, regardless of the source format or location.
Supports a distributed architecture that allows users to deploy data processing and analytics capabilities closer to the data source, reducing latency and improving performance.
Choose a deployment option (Cloud, Self-Managed).
Set up an MQTT broker instance (Serverless, Starter, or Enterprise).
Configure MQTT clients (devices, sensors, applications) to connect to the broker.
Define MQTT topics and message formats for data streaming.
Implement data contextualization rules using the Unified Namespace.
Integrate with analytics platforms or AI models for data analysis.
Configure automated actions based on insights derived from the data.
All Set
Ready to go
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"Customers praise HiveMQ for its reliability, scalability, and comprehensive feature set, particularly in demanding industrial environments."
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Fast distributed SQL query engine for big data analytics.

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