The real-time data streaming foundation for industrial operations

Industrial data streaming is the continuous, real-time movement of operational data, from sensors, PLCs, SCADA, and similar systems, across edge, on-premises, and cloud environments, instead of collecting it in batches for later analysis. It's the foundation that makes real-time visibility, contextualization, and governed action possible.

The MQTT broker is the connectivity layer that makes industrial data streaming possible. It uses a publish/subscribe model to decouple data producers, such as sensors, PLCs, and edge devices, from the systems and applications that consume that data, so information moves continuously in real time instead of in scheduled batches. Clustered persistence, guaranteed message delivery, and support for constrained or unreliable networks let the broker move data from edge to cloud with zero data loss. This is the Connect layer HiveMQ's platform builds on to contextualize, analyze, and act on operational data.

A data historian typically stores time-series data for later, historical analysis, offering a rear-view mirror. Data streaming moves data as it's generated, so it can be acted on immediately and also feed real-time governed intelligence and action, not just retrospective reporting.

No. HiveMQ's approach is additive, not rip-and-replace, which means it brings data in from the systems you already run, without requiring you to disrupt what's currently working or migrate off existing infrastructure.

HiveMQ is proven at scale in automotive and discrete manufacturing, pharmaceutical manufacturing, energy and infrastructure, and food and beverage, among others, with mission-critical deployments at Audi, BMW, Eli Lilly, Hershey's, Mercedes-Benz, and Ford.

HiveMQ's MQTT broker is certified to ISO 27001 and SOC 2, with enterprise security including TLS, authentication, and role-based access control (RBAC) enforced at the broker level for every client and user.

MQTT is a lightweight publish/subscribe protocol purpose-built for constrained edge and OT devices, while Kafka is typically used for high-throughput enterprise event streaming. HiveMQ integrates with Kafka and similar systems so governed industrial data can move into the tools your enterprise and analytics teams already use, rather than treating them as competing choices.

Data Streaming is the Connect layer, the foundation the rest of HiveMQ Platform is built on. Contextualize, Analyze, and Act all run on top of the same trusted data streaming layer, so nothing needs to be re-architected as you adopt more of the platform.