Modern Data Historian

Modernize your legacy data historian by adopting a time series database. Get Industry 4.0-ready, maximize OEE, improve operational efficiency, and reduce costs with real-time data

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Time Series Database

Source: DB Engines

1B+

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See every signal from every asset

Industrial systems generate a constant stream of events, measurements, and status changes. A modern data historian captures and analyzes that high-resolution, high-velocity time series data in real-time, detecting anomalies early, predicting failures, and making proactive decisions that keep operations running smoothly.

Time series reveals the past, sharpens the present, and anticipates the future, driving predictive maintenance that maximizes uptime, efficiency, and reliability.

InfluxDB 3 is purpose-built for time series and Industry 4.0

Flexible deployment

Deploy in the cloud (multi- or single-tenant), on-prem, or at the edge.

Open integration, no lock-in

Connect seamlessly to industrial protocols (MQTT, Kafka) and open formats, avoiding proprietary vendor lock-in.

Edge-first, real-time processing

Detect anomalies and run predictive models at the edge with low latency, even in intermittent connectivity environments.

Low-cost object storage at scale

Retain high-fidelity data for as long as you need with object storage optimized for throughput and cost.

Unlimited scale for predictive maintenance

Analyze millions of unique series per second, delivering the performance required for Industry 4.0 workloads.

Unified operational visibility

Deliver a single source of truth across assets and sites for monitoring, reporting, and root-cause analysis.

Predictive maintenance at a global scale

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Traditional data historians can't keep up

Traditional historians weren’t built for the massive volumes, high cardinality, and real-time demands of Industry 4.0. Burdened by proprietary “black box” designs and siloed, on-prem systems, they’re slow, costly, and hard to integrate, making instant insight and predictive maintenance nearly impossible.

InfluxDB removes these constraints, delivering open, real-time, cost-efficient time series data management at any scale.

Standardizing predictive maintenance at a global scale

Siemens Energy uses InfluxDB to monitor 23,000 battery modules across 70+ sites, analyzing billions of high-frequency sensor readings in real time to ensure quality, prevent downtime, and keep production running—anywhere in the world.

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Real-time maritime and industrial optimization

Everllence (formerly MAN Energy Solutions) uses InfluxDB Cloud to power its MAN CEON platform, analyzing billions of time series data points from connected engines and equipment. CEON detects issues early, guides performance optimization, and drives significant annual fuel savings, advancing Everllence’s mission to decarbonize the maritime sector.

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Scaling renewable energy management in real-time

ju:niz Energy uses InfluxDB Cloud Dedicated to unify data from 30 plants, ingesting 100× more sensor data per second while cutting storage costs by 10×. The platform enables predictive maintenance, deeper operational insights, and smarter renewable energy adoption across its decentralized energy systems.

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Supporting distributed energy resources

Scottish Power Energy Networks (SPEN) replaced its legacy data historian with InfluxDB to handle the surge in data volume and high-cardinality metadata driven by distributed energy resources (DER) adoption. InfluxDB unifies analog telemetry and digital event data in a single platform, delivering real-time insights and fulfilling strict regulatory reporting requirements.

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Standardizing predictive maintenance at a global scale +

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Real-time maritime and industrial optimization +

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Scaling renewable energy management in real-time +

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Supporting distributed energy resources +

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Open connectivity from edge to cloud

InfluxDB is open by design, connecting seamlessly to your existing systems with modern protocols like MQTT and Kafka, open formats, and 300+ Telegraf integrations. Integrate with any tool—AI/ML, visualization, or custom apps—without lock-in.

Deploy anywhere

Whether you're building on-prem, private cloud, edge, or multi-tenant cloud, InfluxDB meets developers where they are.

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Additional resources

Time Series Buying Guide for IIoT

The InfluxDB MCP server connects InfluxDB 3 to AI tools like Claude Desktop via the Model Context Protocol (MCP), making time series data as easy to use as it is to describe.

Learn more

Modernizing Your Data Historian with InfluxDB

The InfluxDB 3 Processing Engine triggers actions in real time, on demand, or on a schedule—ideal for transformation, normalization, alerting, downsampling, and edge replication.

Learn more

Modernizing the Tech Stack for a Modern Utility Grid: Scottish Power Energy Networks Journey with Capula and InfluxDB

Easily start and scale with InfluxDB 3 Enterprise. Or you can upgrade from InfluxDB 3 Core in seconds.

Learn more

FAQ

What is a modern data historian, and how does it differ from a traditional one?

A modern data historian is a time series database purpose-built to collect, store, and analyze high-frequency operational data from industrial assets at Industry 4.0 scale. Unlike traditional historians (OSI PI, AVEVA, Wonderware), which rely on proprietary formats, closed APIs, and per-tag licensing, a modern historian is open by design — supporting standard protocols like MQTT and OPC-UA, running across edge and cloud environments, and exposing data via SQL without vendor-specific tooling.

Why are legacy data historians a problem for Industry 4.0 workloads?

Legacy historians were built for plant-level visibility at polling frequencies of 1–15 minutes — not for the sub-second telemetry, multi-site consolidation, and AI/ML pipelines that modern industrial operations require. Three structural problems drive most modernization conversations: they don't scale well beyond a single plant, they block access to OT data from modern tools like Python, Grafana, and Power BI, and their per-tag licensing model makes every new sensor or higher-frequency capture a direct cost penalty.

Does adopting InfluxDB mean replacing the existing historian entirely?

Not necessarily, and often not at first. The most common approach is a sidecar deployment: the legacy historian stays in place handling established OT workflows, GxP/regulatory data, and existing SCADA historization, while InfluxDB handles the workloads the historian can't — high-frequency sensors, predictive maintenance, cross-site analytics, and AI/ML pipelines. This reduces risk and preserves existing investments while unlocking new capabilities. Full replacement is also possible and has been done by customers like Scottish Power Energy Networks and Teréga.

Where can InfluxDB 3 be deployed for historian workloads?

InfluxDB 3 supports on-premise, edge, and cloud deployments — including single-tenant cloud (Cloud Dedicated), multi-tenant serverless, and self-managed on Linux, Mac, or Docker. For industrial environments with intermittent connectivity or data sovereignty requirements, InfluxDB 3 can run at the edge for local ingestion and real-time operations, while replicating to a cloud hub for long-term storage, cross-site analytics, and enterprise reporting.

How does InfluxDB 3 connect to existing industrial systems and protocols?

InfluxDB 3 connects via Telegraf, which includes 400+ input plugins covering MQTT, Kafka, OPC-UA, Modbus, and most industrial data sources. For brownfield OT environments that also need tag mapping, asset contextualization, and standardization across sites, partner platforms like Litmus Edge provide 250+ native connectors to PLCs, SCADA, DCS, and CNC systems alongside InfluxDB. InfluxDB handles connectivity for many environments out of the box; Litmus and similar partners address the more complex integration and semantic layer requirements.

What scale does InfluxDB 3 support for historian workloads?

InfluxDB 3 ingests millions of unique time series per second with no per-tag limits, and uses object storage for long-term, high-fidelity data retention at low cost. Siemens Energy uses it to monitor 23,000 battery modules across 70+ sites in real time. ju:niz Energy ingests 100× more sensor data per second compared to their previous system while cutting storage costs by 10x.

Does InfluxDB 3 support predictive maintenance and AI/ML, or is it primarily a storage layer?

Both. InfluxDB 3 Enterprise includes a Python-based Processing Engine that runs transformation, normalization, alerting, downsampling, and ML inference directly inside the database — triggered in real time, on a schedule, or on demand. Data is queryable via SQL, which means it connects directly to Python, Databricks, Grafana, Power BI, and other analytics tools without proprietary middleware or OT team involvement.

How does InfluxDB 3's cost model compare to a traditional historian?

Traditional historians like OSI PI and AVEVA use per-tag licensing, where every new sensor, higher-frequency capture, or expanded instrumentation increases cost directly. InfluxDB 3 uses compute-based pricing with no tag limits, so organizations can add sensors, increase data resolution, and scale to new sites without a corresponding cost penalty. InfluxDB 3 Core is also available as open source under Apache 2.0, with a free 30-day trial of InfluxDB 3 Enterprise for organizations evaluating the full platform.

Modern Data Historian

See every signal from every asset