From Edge to Enterprise: How Litmus and InfluxDB Are Modernizing the Industrial Data Stack

By Ben Corbett
Apr 20, 2026 Company Product

Today at Hannover Messe, InfluxData is announcing a strategic partnership with Litmus to address one of the most persistent challenges in industrial data: getting reliable, contextualized telemetry from the shop floor into production systems.

Litmus bridges the gap between OT systems and modern IT infrastructure, while InfluxDB serves as the industrial data hub, giving organizations both real-time operational visibility and enterprise-scale historical analysis in a unified architecture.

By integrating Litmus Edge with InfluxDB 3 Enterprise, teams can collect and contextualize data at the source, then write it into a time series engine built for high-resolution data. Litmus handles connectivity and data normalization at the edge. InfluxDB provides high-throughput ingestion, real-time querying, and cost-efficient long-term storage, deployable at the edge, in the enterprise layer, or both.

The result is a system that captures every signal, retains its context, and makes it immediately usable

The industrial data problem

Something has shifted in industrial sectors. Modernization is no longer a roadmap item, but it’s starting to hit real constraints. The pull: industrial AI initiatives, predictive maintenance, cross-site analytics, digital twins, offer attractive value propositions. The push: legacy data historians are buckling under the demands of modern industrial operations, and the cost of extension is becoming harder to justify.

OT environments are notoriously fragmented. PLCs, CNCs, SCADA systems, and sensors operate across different protocols, vendors, and network boundaries. Getting that data into a usable, consistent format still requires heavy integration, time, and cost.

Traditional Historians made progress on the industrial data problem, but they weren’t built for what comes next. They struggle to preserve context across systems, degrade under high-frequency ingest and query load, and make cross-site analysis slow and expensive. This forces teams into trade-offs between fidelity, scale, and cost.

That’s the core issue: the value of industrial data is in its resolution and context. Most systems weren’t designed to retain either at scale.

How Litmus and InfluxDB work together

To move forward, teams need an architecture built for how industrial data actually behaves: high-frequency, distributed, and context-dependent. Litmus Edge and InfluxDB 3 Enterprise provide that foundation by collecting and structuring data at the edge, then making it available centrally without losing resolution or context.

Here’s how that looks in practice:

• 250+ prebuilt industrial connectors. Out-of-the-box connectivity to industrial data sources, including legacy systems and proprietary protocols. No custom integration required.
• Collect and contextualize at scale. Normalize and contextualize telemetry from the source, with unlimited cardinality that preserves full context without compromising query performance.
• Centralized data, not silos. Bring telemetry from tools, teams, and sites into a single architecture, from single-site monitoring to cross-plant analytics, without a data consolidation project.
• Buffered, store-and-forward data transfer. Buffer and transmit data from remote sites with intermittent connectivity, with no loss or manual recovery.
• Retain more, spend less. Keeps high-resolution data accessible long-term with object storage, without driving up storage costs as you scale.

The edge: collect, contextualize, buffer

Litmus Edge acts as the intelligence layer between your machines and the rest of your data architecture. With 250+ native connectors spanning OPC-UA, Modbus, MQTT, FANUC, Siemens S7, and more, it connects directly to industrial sources (PLCs, CNCs, DCS, SCADA systems, sensors, and beyond) without custom integration.

But connectivity alone isn’t enough. Raw signals without context aren’t useful. Litmus Edge tags, enriches, and structures data at the point of collection so a temperature reading is tied to an asset, production line, facility, and product run. By the time it leaves the edge, it’s already queryable.

The industrial data hub: Centralize, scale, retain

InfluxDB 3 serves as the system of record for industrial time series data, whether deployed at the edge, centralized in the enterprise layer, or both.

At the site level, InfluxDB runs locally alongside Litmus Edge, ingesting full-resolution telemetry and serving low-latency queries for real-time operations. It operates autonomously, so if connectivity to the central hub is interrupted, data is buffered locally and automatically forwarded when the connection is restored. There’s no data loss or manual intervention.

At the enterprise level, a centralized InfluxDB cluster aggregates data from every site into a single query layer across assets, plants, and time horizons. This creates a consistent, high-resolution data layer that can be used across operations, analytics, and industrial AI.

The bridge to higher-level analytics

With high-resolution, contextualized data available across systems, teams can move beyond basic monitoring. Predictive maintenance, anomaly detection, and cross-site analytics all depend on full-fidelity data. Industrial AI at the edge depends on low-latency access to it. Without that foundation, these systems don’t operate reliably. That’s what this architecture enables.

Get started

Whether you’re starting a greenfield initiative or hitting the limits of your current industrial data infrastructure, we’d love to talk.

Reach out to connect to an expert or join the conversation in the InfluxData Community Forums where our team and broader community are active.

If you’re attending Hannover Messe, come find me at the Litmus booth (Stand A09 in Hall 16) and see the architecture running end-to-end.