From Edge to Cloud: How Litmus Edge and InfluxDB Unlock Industrial Intelligence at Hannover Messe

By Ben Corbett
Apr 16, 2026 Demo Product Developer

If you’ve spent time in industrial environments, you know the problem isn’t a lack of data. It’s collecting it reliably, contextualizing it, and storing it at scale. Most stacks weren’t built to fight all three battles.

The industrial data problem

Industrial connectivity is no joke. OT environments are notoriously fragmented and siloed, spanning PLCs, CNCs, SCADA systems, and sensors, each speaking a different protocol, running on a different vendor’s stack, and operating in a network zone that was never designed to talk to anything outside the shop floor. Extracting value from that data has traditionally required heavy IT involvement, expensive integrations, and months of professional services work, and the traditional answer was usually a historian. Historians made progress on the access problem, giving individual sites a way to capture and store machine data. But standardizing that data across silos and contextualizing it across systems and plants is where they fall short. And unfortunately, that’s where most of the value lies.

Once data is collected and contextualized, the next problem is keeping it useful at scale. This is more than a storage problem. Sustaining high-frequency ingest of contextualized telemetry and querying that data fast enough to act on it is where most systems break. Historians were not designed for this. They sacrifice resolution, degrade under query load, and make cross-site, cross-system analysis slow and impractical. The value in industrial data is in the detail, and most platforms are architected to throw this detail away.

Collect, contextualize, and store—all at the edge

Litmus Edge acts as the intelligence layer between your machines and the rest of your data architecture. It connects natively to hundreds of industrial protocols, including OPC-UA, Modbus, MQTT, FANUC, Siemens S7, and many more, normalizing disparate machine data into a unified, consistent stream.

But connectivity alone isn’t enough. Raw machine signals mean little without context. Litmus Edge allows operations teams to tag, enrich, and structure data at the point of collection. A temperature reading becomes tied to a specific asset, production line, facility, and product run. By the time data leaves the edge, it is no longer just a number. It is a meaningful, queryable event.

Scale, query, retain: your industrial data hub

InfluxDB 3 becomes the system of record for your industrial time series data at the edge, in a centralized environment, or both.

It ingests high-frequency telemetry at full resolution, serves low-latency queries for real-time operations, and scales to fleet-wide analysis across sites and time horizons without forcing tradeoffs between fidelity and cost. High cardinality isn’t a problem to design around. Long-term retention doesn’t require a cost penalty. The data stays detailed, queryable, and useful.

Scaling across lines, sites, and the enterprise

Scale changes what’s possible, but only if the data model scales with it. When every site collects and contextualizes data the same way, writing to a consistent schema, cross-site analysis becomes straightforward. Comparing performance across plants, identifying outliers, and correlating signals across a global fleet become simple queries instead of integration projects. That consistency is what the Litmus and InfluxDB architecture is designed to deliver.

• Which production lines across all facilities are showing early indicators of equipment degradation?
• How does energy consumption per unit compare across sites running similar processes?
• Where are the outliers? And what can the top performers teach the rest of the network?

These are not hypothetical future capabilities. They are available today to any organization willing to invest in getting the data foundation right.

The bridge to higher-level analytics

InfluxDB doesn’t just store data well; it integrates cleanly with the ecosystem: the analytics, visualization, and AI/ML tooling your teams are already investing in. Grafana dashboards, anomaly detection workflows, and digital twin platforms connect through InfluxDB’s SQL-native interface and open APIs without custom pipelines or bespoke integration work.

For OT teams, that’s the point. The edge handles the hard part—protocol translation, normalization, enrichment. InfluxDB centralizes the results into a single, interoperable data layer that every team can query with the tools they already use.

The result is a data architecture that is genuinely interoperable; the plant floor and the enterprise layer are finally speaking the same language.

Extending into the cloud with AWS

There are several ways to deploy InfluxDB as your industrial data hub: on-premises, at the edge, or in the cloud. For teams who want to go straight to the cloud, AWS is a natural fit. In this reference architecture, Litmus Edge writes contextualized telemetry directly into Amazon Timestream for InfluxDB, creating a seamless path from the shop floor to cloud-scale analytics. This allows teams to centralize access, scale analytics, and integrate with the broader AWS ecosystem without rebuilding their infrastructure from scratch.

Once data is available in AWS, it opens up a broader set of capabilities. For example, as new data arrives, you can trigger serverless workflows with AWS Lambda, stream high-velocity data through Kinesis for downstream processing, or connect directly to SageMaker to train models on high-fidelity data, without reshaping or downsampling it first.

What we’re showing at Hannover Messe

At Hannover Messe, you’ll be able to see this architecture running end-to-end:

• Litmus booth (Hall 16, Stand A09): The full Digital Factory demo, showing how data flows from industrial systems into Litmus and into InfluxDB 3 Enterprise in real-time.
• InfluxData kiosk (within the Litmus booth): A deeper look at how InfluxDB handles high-frequency ingest, real-time querying, and efficient storage at massive scale.
• AWS booth (Litmus kiosk): The cloud extension of the demo, highlighting replication into Amazon Timestream for InfluxDB and integration with AWS services.

The InfluxData team (including myself) will be on-site at the Litmus booth throughout the event to walk through the architecture and discuss real-world deployment patterns.

Post by Ben Corbett, InfluxData; Rajesh Gomatam, Ph.D. Principal Partner Solutions Architect - Manufacturing, AWS; and Benjamin Norman, Partner Solution Architect, Litmus