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Create a real-time
data backbone

Every system reads from one live source the moment data changes, so a value reaches the dashboard, the model or the person who needs it while it still means something.

You cannot get data to the right place at the right time

Locked where it is made
Controllers and historians speak protocols that were never meant to leave the floor, so the data exists and nothing above the line can reach it.
Arrives already stale
Polled on a cycle rather than sent when it changes, so by the time a value lands the moment it described has passed.
One destination at a time
Every new consumer means another point-to-point connection to build and keep alive, so most systems never get data they could use.

Connect moves operational data where it is needed, the moment it changes

Edge gateway

Bring OPC UA, Modbus, S7 and BACnet onto the platform at the source, as software rather than an appliance on every line.

So you can

Reach legacy assets without per-line middleware or a Windows-bound gateway, so line launches and retrofits stop needing bespoke connectivity engineering.

MQTT data plane

One event-driven backbone every system publishes to and subscribes from, moving data the moment it changes rather than on a poll cycle.

So you can

Keep data through WAN loss and broker failure with no gaps to reconcile, so downtime and OEE numbers stop being reconstructed after the fact.

Bidirectional bridging

Site to region to cloud on one path, in both directions, with store-and-forward so nothing is lost when a link drops.

So you can

Run one architecture across the fleet rather than one per site, so site N+1 costs materially less than site one.

Enterprise security & access control

TLS and mTLS, topic-level permissions, role-based access and an audit trail, designed for segmented OT networks.

So you can

Carry every flow over one governed boundary, so the default answer to a new project stops being another firewall exception.

Enterprise integration

Pre-built paths into Kafka, databases, historians and cloud services, so the platform feeds what you already run.

So you can

Land operational data in the systems you already run, so a new use case goes from a project to a configuration change.

Deployed where it has to be

Self-managed on Kubernetes or OpenShift, or fully managed on AWS, Google Cloud or Azure in the region you choose.

So you can

Stop the deployment model killing the evaluation, so a sovereignty constraint and a cloud mandate land on the same architecture.

What this changes

Real-time visibility across the estate

Edge, on-premises and cloud in one place, instead of stitching together the blind spots between siloed systems.

Faster onboarding for new sites

No custom integration to rebuild from scratch every time a plant or a line comes online.

An integration backlog that burns down

Each new consumer is a subscription, so the number of integrations stops scaling with the number of systems.

The specifics underneath

Messaging
MQTT 3.1.1 and 5.0, QoS 0, 1 and 2, persistent sessions, shared subscriptions, retained messages
OT protocols
OPC UA, Modbus, Siemens S7, Beckhoff, BACnet, EtherNet/IP and MTConnect, plus an SDK for adapters you write yourself
Security
TLS 1.2 and 1.3, mutual TLS, role-based access control, OAuth 2.0 and LDAP, topic-level permissions and an audit trail
Integrations
Sixteen supported enterprise extensions, including Kafka, Kinesis, Pub/Sub, Snowflake, MongoDB, PostgreSQL and OpenTelemetry tracing
Deployment
Self-managed on your own infrastructure, Kubernetes or OpenShift, or fully managed on AWS, Google Cloud or Azure

See it in the platform

Screenshot of the HiveMQ Platform Connect workspace, showing network health, connections, throughput, and a table of brokers and their status.

The components underneath

HiveMQ Broker

The enterprise MQTT broker for high-scale, reliable messaging across sites and clouds.
Explore HiveMQ Broker →

HiveMQ Edge

A software gateway that brings OT protocols onto the platform at the source.
Explore HiveMQ Edge →

HiveMQ Extensions

Pre-built integrations into the IT systems you already run.
Explore HiveMQ Extensions →
HiveMQ has delivered the stability and reliability that we require for 4 years now and it has not gone down.

Marius Hertfelder

Chief Software Architect, Mercedes-Benz

Your data is flowing. Next, give it shared meaning.

You are here

Connect

Move operational data reliably across OT, IT, edge and cloud.

Step 2

Contextualize

Give every signal shared meaning and a governed structure.

Continue to Contextualize →
Step 3

Analyze

Turn trusted data into operational intelligence.

Step 4

Act

Put that intelligence to work in governed workflows.

Start with one operational improvement. Scale the results.

Start with the outcome that matters most. Prove value on the data you already have, then scale the pattern.