'%20stroke-width='2'%20stroke-miterlimit='10'/%3e%3cpath%20d='M150.903%20601.339L494.903%205.33911'%20stroke='url(%23paint1_linear_275_1470)'%20stroke-width='2'%20stroke-miterlimit='10'/%3e%3cpath%20d='M400.073%20212.339C386.406%20203.81%20386.895%20195.18%20392.073%20185.339L381.573%20201.839L400.073%20212.339Z'%20fill='%23FFC000'/%3e%3cpath%20d='M368.573%20226.339C380.573%20208.339%20390.573%20208.339%20396.573%20212.339L386.073%20203.339L381.573%20201.839L368.573%20226.339Z'%20fill='%23FFC000'/%3e%3cpath%20d='M369.573%20220.839C379.173%20204.439%20371.573%20197.1%20366.573%20195.1L377.073%20197.839L380.573%20201.339L380.073%20206.839L369.573%20220.839Z'%20fill='%23FFC000'/%3e%3cpath%20d='M632.828%20262.442L532.844%200.999929'%20stroke='url(%23paint2_linear_275_1470)'/%3e%3cpath%20d='M0.986313%20614.793L641.681%20352.645'%20stroke='url(%23paint3_linear_275_1470)'/%3e%3cdefs%3e%3clinearGradient%20id='paint0_linear_275_1470'%20x1='27.2367'%20y1='-1.55603'%20x2='640.044'%20y2='345.503'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20offset='0.00912027'%20stop-color='%23FFC000'%20stop-opacity='0'/%3e%3cstop%20offset='0.519305'%20stop-color='%23FFC000'/%3e%3cstop%20offset='0.652534'%20stop-color='%23FFC000'/%3e%3cstop%20offset='1'%20stop-color='%23FFC000'%20stop-opacity='0.35'/%3e%3c/linearGradient%3e%3clinearGradient%20id='paint1_linear_275_1470'%20x1='489.286'%20y1='13.5365'%20x2='156.244'%20y2='594.518'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%23FFC000'%20stop-opacity='0'/%3e%3cstop%20offset='0.2419'%20stop-color='%23FFC000'/%3e%3cstop%20offset='0.398863'%20stop-color='%23FFC000'/%3e%3cstop%20offset='0.945888'%20stop-color='%23FFC000'%20stop-opacity='0.3'/%3e%3c/linearGradient%3e%3clinearGradient%20id='paint2_linear_275_1470'%20x1='734.462'%20y1='184.783'%20x2='633.302'%20y2='277.762'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%23FFC000'/%3e%3cstop%20offset='1'%20stop-color='%23FFC000'%20stop-opacity='0.2'/%3e%3c/linearGradient%3e%3clinearGradient%20id='paint3_linear_275_1470'%20x1='1.33593'%20y1='614.522'%20x2='-21.2045'%20y2='530.188'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%23FFC000'%20stop-opacity='0.2'/%3e%3cstop%20offset='1'%20stop-color='%23FFC000'/%3e%3c/linearGradient%3e%3c/defs%3e%3c/svg%3e)
Industrial data streaming should be real-time, secure, reliable and scalable. Get data from where it's created to where it's needed, in real time. In practice, most industrial environments still fight the same five barriers.
Industrial data lives across historians, SCADA, SAP and similar systems that don't talk to each other; some assets aren't connected at all. Teams switch between tools just to see what's currently running.
Point-to-point integrations break under change, and teams get caught between the plant floor and IT/security every time something needs to move.
Legacy PLCs and protocols leave gaps where assets simply aren't connected, and outages mean data gets lost instead of buffered.
Raw uncontextualized data is moved away from its source, and any context and governance is reconciled as a separate process retroactively.
Dimension | Legacy approach | HiveMQ data streaming |
|---|---|---|
Products | Separate data broker, edge, and integration tools, each with its own UI | One platform, one account, one console |
Data flow | Passive, one-way, rear-view mirror | Bi-directional; event-driven |
Reliability | Fragile point-to-point connections that break under change | Reliable, secure streaming across edge, on-prem, and cloud |
Governance | Data cleanup and governance done as post-processing; medallion architecture | Governance is done in real-time, and enforced at every layer so data is always valid right from the source |
Outcome | More data, little insight | Trusted, real-time data available wherever it's needed |
Data Streaming is the Connect layer of HiveMQ Platform: the foundation everything else in the platform runs on.
Try for free
HiveMQ provides real-time visibility across cloud, on-premises, and edge in a single platform, so teams work from current, unified context rather than reconciling disconnected systems after the fact.
Brings together data from PI, SCADA, SAP and similar systems into one place, on HiveMQ's reliable, secure and scalable broker.
Deploys across cloud, self-managed, and hybrid environments, so architecture decisions aren't forced by the vendor.
Runs agentic AI applications across millions of concurrent connections and hundreds of sites without re-architecture.
Proven at scale and trusted for mission-critical operations by Audi, BMW, Eli Lilly, Hershey's, Mercedes-Benz, and Ford. 100% MQTT-compliant, and certified to ISO 27001 and SOC 2.
Additive, not rip-and-replace. Brings OT data in from the floor without disrupting operations, and buffers through outages instead of losing data.
Distributed sites, latency, and sovereignty mean data can't all go to the cloud. HiveMQ streams and acts across edge, on-prem and cloud, with distributed execution and central governance, for real-time operations without a central bottleneck.
HiveMQ Edge brings OT data in from the floor via protocol adapters and offline buffering, without disrupting operations or requiring a wholesale upgrade of legacy PLCs.
A single view across every MQTT broker, cloud, and self-hosted deployment, in one place, so architects stop rebuilding a one-off integration for every new site.
High-fidelity, real-time data close to the asset feeds predictive maintenance models with production-grade inputs instead of raw, patchy feeds.
Move governed industrial data into the enterprise and analytics systems teams already use, such as Kafka, cloud, and databases, without exposing raw OT data directly.
Only one-third of respondents have production-grade real-time data streaming today, underscoring the need for event-driven architectures that deliver live operational data to AI and analytics instead of relying on batch exports.
Accelerating Industrial AI in 2026 Report
A technical white paper on the four-stage Connect, Contextualize, Analyze, Act architecture for turning fragmented manufacturing data into real-time operational intelligence and a governed foundation for agentic AI.
Real-time data streaming, governed operational data, and the right architecture. Learn the industrial AI playbook that turns pilots into platforms.
Learn how ontology-driven AI agents use semantic models, knowledge graphs, and structured data to enable reliable, scalable agentic automation in industrial operations.
Choose between a fully-managed cloud or self-managed platform. Our experts can help you with your solution and demonstrate HiveMQ in action.
