InfluxData Blog - Developer

Less Friction, More Control: Here's What Shipped in Q1

Ryan Nelson (InfluxData) — Wed, 08 Apr 2026 08:00:00 +0000

Our Q1 momentum has been focused on a simple goal: making InfluxDB easier to operate, easier to scale, and faster to put to work.

Across Telegraf, InfluxDB 3, and our managed offerings, these updates reduce friction in how teams collect, process, and scale time series workloads.

Telegraf Controller enters beta

Telegraf is already a powerful way to collect metrics, logs, and events across environments. At scale, the challenge shifts from collection to control. Telegraf Enterprise is designed to solve that problem.

At the center is Telegraf Controller, a control plane that gives teams centralized configuration management and fleet-wide health visibility. The beta includes major capabilities such as API authentication, API token management, user account management, multi-user support, role-based access control, global settings management, and expanded plugin support in the visual config builder.

Feedback from early users is shaping the road to general availability, with enterprise licensing, enforcement, audit logging, and federated identity management next on the roadmap. Sign up to join the beta.

InfluxDB 3.9 adds more operational control

Last week’s release of InfluxDB 3.9 is focused on making the platform easier to run at scale, with improvements aimed at predictability, visibility, and day-to-day management. The release expands CLI and automation support for headless environments, improves resource and lifecycle management, and adds clearer visibility into access control and product identity across Core and Enterprise deployments. These are the changes that matter in production: fewer rough edges, stronger operational clarity, and better control as workloads grow.

InfluxDB 3.9 Enterprise also includes a new beta performance preview for non-production environments. This optional preview includes optimized single-series queries, reduced CPU and memory spikes under load, support for wider and sparser schemas, and early automatic distinct value caches to reduce metadata query latency. These features are not yet recommended for production, but they give customers an early look at capabilities planned for future releases and a chance to help shape what comes next.

Processing Engine updates make InfluxDB 3 easier to operationalize

The Processing Engine remains one of the most powerful parts of InfluxDB 3 because it allows teams to run logic directly at the database. Users can transform data on ingest, run scheduled jobs, or serve HTTP requests without adding external services or layering on more pipeline complexity.

This quarter, we continued to expand both the engine itself and the plugin ecosystem around it. The latest plugins make it easier to get data into InfluxDB 3 from more sources:

The Import Plugin provides a simpler path for bringing data from InfluxDB v1, v2, or v3 into InfluxDB 3 Core and Enterprise, with support for dry runs, progress tracking, pause and resume, conflict handling, and flexible filtering.
New MQTT, Kafka, and AMQP subscription plugins help users ingest streaming data directly from external message brokers.
The new OPC UA Plugin gives industrial teams a more direct path to data from PLCs, SCADA systems, and other OPC UA-enabled equipment.

We also made important improvements to the Processing Engine itself:

New synchronous write controls give plugin authors more flexibility over durability and throughput.
Batch write support improves efficiency for high-volume workloads.
Asynchronous request handling keeps status checks and control operations responsive during long-running jobs.

Together, these updates make the Processing Engine a more practical way to build and operate real-time data pipelines directly inside InfluxDB 3. Check out our docs to learn more.

Better visibility for Cloud Dedicated customers

As teams run production workloads on Cloud Dedicated, understanding how the system is being used becomes just as important as performance itself.

This quarter, we introduced:

Query History (GA) for troubleshooting, performance analysis, and deeper insight into query activity.
S3 API dashboards (Tier 1 and Tier 2), including monthly usage visibility.

These updates give teams better visibility into system behavior, usage patterns, and a faster path to understanding activity across the environment. Detailed docs here.

InfluxDB Enterprise 1.12.3 delivers efficiency gains for v1 environments

For teams needing more performance and running large-scale v1 Enterprise environments, InfluxDB Enterprise 1.12.3 is now available with substantial improvements in efficiency and reliability:

100x faster retention enforcement for high-cardinality datasets
30% lower CPU usage during compaction
5x faster backups with configurable compression
3x less disk I/O during cold shard compactions

These improvements make Enterprise v1 clusters more efficient, more predictable under load, and more cost-effective to operate. Read the release notes.

Amazon Timestream for InfluxDB adds a new scale tier and simple upgrade path

InfluxDB 3 on Amazon Timestream for InfluxDB now supports clusters of up to 15 nodes, giving customers a new scale tier for more demanding real-time workloads.

This expanded tier improves query concurrency, increases ingestion throughput, and provides stronger workload isolation across ingestion, queries, and compaction. For teams running high-velocity, high-resolution data in production, that means more headroom to scale without compromising real-time performance.

Customers can also seamlessly migrate from InfluxDB 3 Core to InfluxDB 3 Enterprise, making it easier to move into this higher-performance tier without a manual architectural overhaul or data loss. The new 15-node option is available for InfluxDB 3 Enterprise in all AWS regions where Amazon Timestream for InfluxDB is offered. Read more here.

Looking ahead

Taken together, these updates are about helping teams do more with less friction: move data faster, operate with more confidence, and scale time series workloads without losing control. As operational data becomes more central to modern systems, we are continuing to invest in the infrastructure that turns that data into action across edge, cloud, and distributed environments.

New Plugins, Faster Writes, and Easier Configuration: What’s New with the InfluxDB 3 Processing Engine

Gary Fowler (InfluxData) — Tue, 07 Apr 2026 08:00:00 +0000

The Processing Engine is one of the most powerful features in InfluxDB 3. It lets you run Python code at the database—transforming data on ingest, running scheduled jobs, or serving HTTP requests—without spinning up external services or building middleware. You define the logic, attach it to a trigger, and the database handles the rest.

Since launching the Processing Engine, we’ve been building out both the engine itself and the ecosystem of plugins that run on it. Today, we want to walk you through some exciting recent additions: new plugins for data ingestion, import, and validation; some general improvements to the engine; and a better configuration experience using InfluxDB 3 Explorer.

A quick refresher: Processing Engine plugins

If you’re already familiar with the Processing Engine, feel free to skip ahead. For those newer to the concept, here’s the short version.

A plugin is a Python script that runs inside InfluxDB 3 in response to a trigger. There are three trigger types: data writes (react to incoming data as it’s written), scheduled events (run on a timer or cron expression), and HTTP requests (expose a custom API endpoint). Plugins have direct access to the database: they can query and write without having to egress and ingress the data to a different machine or location. Plugins can also talk to other systems, letting you utilize data from other places or systems.

You can write your own plugins from scratch to solve problems specific to your environment. That’s the whole point of embedding Python in the database: your logic, your rules, running right next to your data.

But we also know that not everyone wants to start from a blank page. That’s why we maintain an official plugin library with production-ready plugins for common time series tasks, such as downsampling, anomaly detection, forecasting, state change monitoring, and sending notifications to Slack, email, or SMS.

These official plugins are designed to work in two ways. You can install them and use them as-is, configuring them through trigger arguments or TOML files to fit your setup. Or you can treat them as templates: fork one, customize the logic, and build something tailored to your exact workflow. Either way, they’re meant to get you moving faster.

One more thing worth mentioning: if you’re thinking about building a custom plugin but aren’t sure where to start, AI tools like Claude can be very effective. Point Claude to the Processing Engine documentation and the plugin library repo for examples, describe what you want your plugin to do, and let it generate a first draft. We’ve seen simple plugins created in a single shot, from description to working code, and even more complex plugins come together quickly when the AI has good examples to work from. It’s a great way to get past the blank-page problem and into something you can iterate on.

New plugins: data ingestion, import, and validation

We’ve recently added several new plugins to the library that address some of the most common requests we’ve been hearing from the community. These are available now in beta—they’re fully functional, but we want to see them tested across more environments before we call them production-ready. Give them a try and let us know how they work for you.

InfluxDB Import Plugin

If you’re running an older version of InfluxDB and want to bring your data into InfluxDB 3, the new Import Plugin makes that significantly easier. It supports importing from InfluxDB v1, v2, or v3 instances over HTTP, with features you’d expect from a serious import tool: automatic data sampling for optimal batch sizing, pause/resume for long-running imports, progress tracking, tag/field conflict detection and resolution, configurable time ranges and table filtering, and a dry run mode so you can preview what an import will look like before committing to it.

The plugin runs as an HTTP trigger, so you control the entire import lifecycle (start, pause, resume, cancel, check status) through simple HTTP requests. That means you can kick off a large import, pause it during peak hours, and pick it up later from exactly where it left off. For small or medium-sized InfluxDB databases, some might even use this as a migration tool to move to InfluxDB 3.

Data subscription plugins: MQTT, Kafka, and AMQP

These three plugins let new InfluxDB 3 users start getting data into InfluxDB 3 fast and without coding. They let you subscribe to external message brokers and begin automatically ingesting that data into InfluxDB 3.

The MQTT Subscriber Plugin connects to an MQTT broker, subscribes to topics you specify, and transforms incoming messages into time series data. It supports JSON, Line Protocol, and custom text formats with regex parsing, and uses persistent sessions to ensure reliable message delivery between executions.

The Kafka Subscriber Plugin does the same for Kafka topics. It uses consumer groups for reliable delivery, supports configurable offset commit policies (commit on success for data integrity, or commit always for maximum throughput), and handles JSON, Line Protocol, and text formats.

The AMQP Subscriber Plugin rounds out the trio with support for RabbitMQ and other AMQP-compatible brokers. Like the others, it supports multiple message formats, flexible acknowledgment policies, and comprehensive error tracking.

OPC UA Plugin

For industrial environments, the new OPC UA Plugin connects directly to PLCs, SCADA systems, and other OPC UA-enabled equipment. It polls node values on a schedule and writes them into InfluxDB 3 with automatic data type detection. You can list specific nodes for precise control, or use browse mode to auto-discover devices and variables across large deployments. The plugin maintains a persistent connection between polling intervals and supports quality filtering, namespace URI resolution, and TLS security.

Now, you might be thinking: “I’m already using Telegraf to interface with my streaming data services or OPC UA, why do I need these?” If Telegraf is working well for you, that’s great; there’s no need to change what isn’t broken. But if you’re newer to InfluxDB and aren’t yet a Telegraf user, these plugins give you another way to quickly get data flowing into InfluxDB 3 without adding another component to your stack.

All three plugins share a consistent configuration model: you can set them up with CLI arguments for simple cases or TOML configuration files for more complex mapping scenarios. They all include built-in error tracking (logging parse failures to dedicated exception tables) and write statistics so you can monitor ingestion health over time.

Schema Validator Plugin

One of the benefits of InfluxDB is that you don’t have to pre-define a schema. Data gets written as it is received. But for some use cases our customers have, they do want to constrain incoming data to conform to a specific schema.

The Schema Validator Plugin addresses that challenge, ensuring only clean, well-structured data makes it into your production tables. You define a JSON schema that specifies allowed measurements, required and optional tags and fields, data types, and allowed values. The plugin sits on a WAL flush trigger and validates every incoming row against your schema. Rows that pass get written to your target database or table; rows that fail get rejected (and optionally logged so you can see what’s being filtered out).

A typical pattern is to write raw data into a single database or table, let the validator check it, and have clean data land in a separate database or table. It’s a straightforward way to build a reliable data pipeline without external tooling.

Processing Engine general improvements

Alongside the new plugins, we’ve made several improvements to the Processing Engine itself that give plugin authors more control over write behavior, throughput, and concurrency.

Synchronous writes with durability control. New synchronous write functions let you choose between two modes: wait for the write to persist to the WAL before returning (for cases where you need to query the data immediately after writing), or return immediately for maximum throughput. This means you can treat bulk telemetry data as a fast path while ensuring that coordination states, such as job checkpoints or configuration flags, are immediately durable and queryable.

Batch writes. If your plugin writes thousands of points, the overhead isn’t in the data itself; it’s in the repeated write calls. The new batch write capability lets you group many records into a single write operation, which can dramatically improve throughput and make memory usage more predictable.

Asynchronous request handling. Request-based triggers now support concurrent execution. Previously, request handlers processed one request at a time, which meant a slow request would block everything behind it. With asynchronous mode enabled, the engine can handle multiple requests concurrently, so status checks, control commands, and other lightweight requests stay responsive even while a heavy operation is running.

These improvements work together in practice. The Import Plugin, for example, uses batch writes with fast-path durability for bulk data transfer, synchronous durable writes for checkpoints and state, and async request handling to keep its pause/resume/status endpoints responsive during long-running imports.

Easier plugin configuration in Explorer

We’ve also been improving InfluxDB 3 Explorer to make configuring plugins simpler, especially for the plugins in the library.

Until now, configuring a plugin meant passing all the right parameters as startup arguments to the Python script or specifying them in a TOML file. That works, but it requires you to know exactly which parameters a plugin expects—which means reading the documentation first.

We’re adding dedicated UI configuration forms for some of the plugins in Explorer. Instead of assembling a string of key-value pairs, you’ll see a form with all the available options laid out, along with descriptions and example values. Required fields are clearly marked, and the form handles the formatting for you. It’s the same configuration under the hood, just a much more approachable way to get there.

This is especially helpful for plugins with more involved configuration, like the data subscription plugins. where you’re specifying broker connections, authentication, message format mappings, and field type definitions. The form-based approach removes the guesswork and lets you get a plugin running without bouncing back and forth between the docs and your terminal. So far, we have built a specific configuration for the Import, Basic Transformation, and Downsampling plugins.

This is what it looks like for the Import plugin:

This is what the Basic Transformation and Downsample configuration looks like:

Look for these to become available in Explorer in the next couple of months.

What’s next

We are continuing to improve the Processing Engine and the Plugin Library. We have an OPC UA plugin about ready for you to try, as well as some additional anomaly detection and forecasting plugins. And, we are building UI configuration for the data subscription plugins mentioned above to make them even easier to configure.

Try them out

All new plugins are now available in beta in the InfluxDB 3 Plugin Library. They require InfluxDB 3 v3.8.2 or later. Install them from the CLI using the gh: prefix, or browse and install them directly from InfluxDB 3 Explorer’s Plugin Library.

We’re releasing these as a beta because we want your feedback. We’ve tested them thoroughly internally, but real-world environments are always more diverse and more demanding than any test suite. If you run into issues, have ideas for improvements, or build something cool on top of these plugins, we’d love to hear from you: drop into the InfluxData Discord, post on the Community Forums, or open an issue on GitHub.

What’s New in InfluxDB 3.9: More Operational Control and a New Performance Preview

Peter Barnett (InfluxData) — Thu, 02 Apr 2026 12:00:00 +0000

We’ve spent the last few months listening to how teams are running InfluxDB 3 in the wild. The feedback was clear: as you scale, you need less “guesswork” and more control. Today’s release of InfluxDB 3.9 is our answer to that.

As more teams move InfluxDB 3 into production, our focus has shifted toward the operational experience: how you manage the database at scale, how you ensure it remains secure, and how you provide a seamless experience for users. This release is packed with a host of quality-of-life improvements and a beta of the key features we have planned for upcoming releases.

Whether you’re using the open source InfluxDB 3 Core for recent data and local workloads or scaling with InfluxDB 3 Enterprise for the full clustering and security suite, these 3.9 updates are designed to make your stack more predictable.

Operational maturity and system transparency

In 3.9, we’ve focused on making the database more predictable and transparent for operators. We have organized these refinements into three key areas:

Advanced CLI & Automation: We’ve expanded the CLI to better support complex, headless environments. This includes new flags for non-interactive automation and data validation, alongside support for unique host overrides to target specific node types in a cluster. We’ve also improved how Parquet query outputs are piped, making it easier to integrate InfluxDB into automated data pipelines.
System Reliability & Resource Management: We’ve refined how the database handles resources and large-scale schemas. To better support complex data, we’ve increased the default string field limit to 1MB. We’ve also hardened the database lifecycle; administrative controls are now more rigorous, and we’ve ensured that background resources, such as triggers, are cleanly decommissioned whenever a database is removed.
Visibility & Under-the-Hood Infrastructure: We’ve upgraded our core infrastructure to improve both security and operational clarity. This includes upgrading DataFusion and the bundled Python for more efficient query execution and plugin security. Additionally, the system now provides better visibility into access control and product identity, updating metrics, headers, and metadata access to clearly distinguish between Core and Enterprise builds across your stack.

Collectively, these refinements remove the subtle points of friction that can accumulate as a system scales in production. By hardening resource management and streamlining automation, we’re ensuring that InfluxDB 3 remains a predictable, “set-it-and-forget-it” core for your infrastructure.

Now in beta: A new performance preview

Behind the scenes, we’ve been working on performance updates to InfluxDB 3. These improvements support large-scale time series workloads without sacrificing predictability or operational simplicity. This work lays the foundation for what’s coming in 3.10 and 3.11, specifically focusing on smoothing behavior under load and expanding the range of schemas InfluxDB 3 can handle.

Because performance in time series is highly dependent on specific workloads and cardinality, we are introducing these updates as a beta in InfluxDB 3 Enterprise. The beta is intended for testing in staging or development environments only. It allows you to explore and provide feedback on:

Optimized single-series queries: Targeting reduced latency when fetching single-series data over long time windows.
Resource smoothing: Testing reduced CPU and memory spikes during heavy compaction or ingestion bursts.
Wide-and-sparse table support: For handling schemas ranging from extreme column counts to ultra-sparse data tables (or any combination).
Automatic distinct value caches: Early-stage, auto-creation of caches designed to reduce friction and eliminate metadata query latency.

These updates are available as an optional, flag-gated preview in InfluxDB 3.9 Enterprise. They are not recommended for production workloads. We encourage Enterprise users to test these capabilities against their specific use cases to help us refine the features for GA. InfluxDB 3 Core will also support many of these new features in the coming releases.

For instructions on how to enable these preview flags and to view the full technical requirements, visit our official Enterprise documentation.

Download InfluxDB 3.9: Available now via our downloads page or latest Docker images.
Join the beta: If you are an InfluxDB 3 Enterprise Trial user, reach out to me in our Discord or Community Slack to learn how to enable these beta features.

What is MRO? Maintenance, Repair, and Operations Explained

Charles Mahler (InfluxData) — Tue, 31 Mar 2026 08:00:00 +0000

MRO stands for maintenance, repair, and operations. It refers to the activities, supplies, and services that keep equipment, facilities, and infrastructure running safely and efficiently. Every industry that relies on physical assets depends on MRO, whether that means replacing a worn bearing on a production line, restocking safety gloves in a warehouse, or servicing an HVAC system in a hospital.

Despite being one of the largest categories of indirect spending in most organizations, MRO is chronically under-managed. This article explains what MRO covers, why it matters, how maintenance strategies differ, and how it plays out across industries.

What is MRO?

MRO is a broad category that encompasses the indirect materials, maintenance activities, and operational support required to keep a business functioning. MRO includes everything from spare parts and lubricants to safety equipment, cleaning supplies, and the labor required to inspect, fix, and service physical assets.

The scope of MRO varies by organization, but it always sits outside of direct production. A replacement motor for a conveyor belt is an MRO item. The raw steel that travels on that conveyor is not. This distinction matters for accounting, procurement strategy, and inventory management.

Common MRO Supplies and Activities

MRO is easier to understand through concrete examples:

Category	Description	Examples
MRO supplies	Parts, materials, and consumables used to maintain equipment and facilities.	Spare parts (bearings, seals, belts, filters, motors), lubricants and greases, fasteners, hand and power tools, electrical components (fuses, contactors, wiring), safety equipment (gloves, goggles, hard hats, respirators), cleaning and janitorial products, adhesives and tapes, and facility consumables (light bulbs, HVAC filters).
MRO activities	Hands-on maintenance and repair work performed on assets.	Routine inspections, lubrication, electrical testing, equipment alignment, welding repairs, painting and corrosion protection, calibration, and full equipment rebuilds.
MRO services	Outsourced or contracted maintenance support.	Third-party maintenance contracts, on-call repair technicians, specialized inspections (non-destructive testing), and outsourced maintenance for complex assets.

Why MRO matters

MRO spending often accounts for a significant share of an organization’s operating costs, yet it receives a fraction of the strategic attention that direct materials get. The numbers make a compelling case for changing that.

The market is massive. The global MRO market was valued at roughly $715 billion in 2025 and is projected to grow steadily through the next decade, driven by aging infrastructure, the rise of predictive maintenance, and increasing demand for operational efficiency.
Downtime is extraordinarily expensive. A 2024 Siemens report found that unplanned downtime costs the world’s 500 largest companies a combined $1.4 trillion per year, roughly 11% of their annual revenues. At a facility level, costs vary by industry, but the averages are sobering: approximately $260,000 per hour in general manufacturing, and over $2 million per hour in automotive production. Even smaller manufacturers typically lose over $100,000 per hour of unexpected downtime.
Equipment failure is the leading cause of downtime. The average manufacturer faces an estimated 800 hours of equipment downtime annually. Equipment failure accounts for roughly 42% of unplanned downtime incidents, and base components like bearings, seals, and motors are the most common culprits. These are precisely the kinds of failures that a well-run MRO program is designed to prevent.
Proactive maintenance pays for itself. Research from McKinsey and others consistently shows that organizations implementing predictive maintenance programs see 18–25% reductions in overall maintenance costs and 30–50% reductions in unplanned downtime. The U.S. Department of Energy has reported a potential ROI of up to 10x on predictive maintenance investments. Reactive repairs, by contrast, cost three to five times more than planned maintenance once you account for emergency labor, expedited parts shipping, and cascading production losses.
Safety and compliance depend on it. Regulatory bodies across industries mandate specific maintenance activities and intervals. Falling behind on MRO creates safety hazards for workers, compliance risk for the organization, and potential legal liability.

Maintenance strategies: preventive, predictive, planned, and condition-based

Organizations typically employ a mix of strategies, and the trend across industries is a steady shift from reactive to proactive, data-driven approaches.

Preventive Maintenance

Preventive maintenance is scheduled work performed at fixed intervals to reduce the likelihood of failure. Oil changes every 500 operating hours, filter replacements every quarter, and belt inspections every month are all preventive activities. The advantage is predictability: you know what work is coming and can plan parts and labor accordingly. The drawback is that you may be replacing components that still have significant useful life remaining, which wastes money and materials.

Planned Maintenance

Planned maintenance is a broader category that includes any maintenance activity scheduled in advance, whether it follows a calendar-based interval, a usage-based trigger, or a condition-based alert. The defining characteristic is that the work is anticipated and resourced before it begins, as opposed to reactive or emergency maintenance. Planned maintenance also encompasses scheduled shutdowns and turnarounds, where equipment is taken offline deliberately for extensive servicing.

Condition-Based Maintenance

Condition-based maintenance (CBM) uses real-time monitoring of equipment health indicators like vibration, temperature, oil quality, and electrical signatures to trigger maintenance only when those indicators show that maintenance is actually needed. Rather than replacing a bearing on a fixed schedule, CBM replaces it when vibration analysis shows degradation has reached a threshold. This approach eliminates much of the waste inherent in time-based schedules while still catching problems before failure.

Predictive Maintenance

Predictive maintenance takes condition-based monitoring a step further by applying machine learning, statistical models, and trend analysis to forecast when a component is likely to fail. Where CBM reacts to current conditions, predictive maintenance anticipates future conditions based on patterns in historical and real-time data. Sensors tracking vibration, temperature, pressure, and acoustic signatures feed data into analytics platforms that can predict failures days or weeks in advance.

The results are striking: organizations with mature predictive maintenance programs report 35–45% reductions in unplanned downtime and an average ROI of around 250% within the first 18 months.

The movement from reactive to predictive maintenance is one of the defining trends in MRO. As IIoT sensors become cheaper and more accessible, even smaller manufacturers can begin shifting toward condition-based and predictive approaches.

MRO in manufacturing

In the manufacturing industry, MRO encompasses all indirect materials and maintenance activities required to keep a production facility running. It is everything that supports the production process without becoming part of the finished product.

Manufacturing MRO spending is often highly fragmented. A single plant might purchase thousands of distinct SKUs, such as motor drives, conveyor belts, lubricants, rags, and safety boots, from dozens of suppliers. The proportion of organizations using more than 250 MRO suppliers has grown from 6% to 15% in recent years. This fragmentation makes it difficult to negotiate volume discounts, track usage, or identify waste.

Common MRO priorities in manufacturing include reducing unplanned downtime on production lines, maintaining critical spares inventory for high-impact equipment, shifting from reactive to preventive or predictive maintenance, standardizing parts and suppliers to simplify procurement, and ensuring compliance with OSHA and environmental regulations.

Manufacturers that invest in structured MRO programs typically see improvements in overall equipment effectiveness (OEE), lower maintenance costs per unit of output, and fewer safety incidents.

MRO in aviation

Aviation has one of the most rigorous and regulated MRO environments of any industry. Aircraft MRO is governed by strict regulatory frameworks like the FAA in the United States and EASA in Europe. Every maintenance activity must be performed by certified repair stations, documented in detail, and traceable.

The four main categories of aviation MRO are airframe maintenance, engine maintenance, component maintenance, and line maintenance.

Aviation MRO is also where data-driven maintenance has seen some of its most advanced applications. Airlines use predictive maintenance platforms that analyze sensor data from aircraft systems to forecast component failures before they occur, minimizing aircraft-on-ground events and improving safety.

MRO in energy and utilities

Energy and utilities represent one of the most asset-intensive sectors for MRO. Power plants, refineries, pipelines, water treatment facilities, and electrical grids all require continuous maintenance to remain operational and safe.

The consequences of downtime in energy are particularly severe. Utilities face additional complexity from regulatory oversight and public safety requirements; a failed transformer or water treatment system affects entire communities.

This sector has been an early adopter of IIoT and predictive maintenance technologies. Real-time monitoring of turbines, generators, transformers, and pipeline infrastructure allows operators to detect degradation early and schedule maintenance during planned outages rather than responding to emergencies.

MRO procurement, inventory, and software

Three operational areas determine how well an MRO program actually performs on a day-to-day basis.

Area	Description and Key Strategies
Procurement	The process of sourcing and purchasing indirect materials. High transaction volume but low individual dollar value. Improvement strategies include consolidating suppliers, using blanket purchase orders, and implementing e-procurement platforms.
Inventory	Balancing part availability against carrying costs. Effective management relies on criticality-based stocking, min/max levels, and regular cycle counts. MRO inventory supports production but is not part of the finished product.
Software	Tools to plan, track, and optimize maintenance. Includes CMMS for work orders, EAM for lifecycle planning, and e-procurement tools to streamline purchasing.

The process of sourcing and purchasing indirect materials. High transaction volume but low individual dollar value. Improvement strategies include consolidating suppliers, using blanket purchase orders, and implementing e-procurement platforms.

Inventory

Balancing part availability against carrying costs. Effective management relies on criticality-based stocking, min/max levels, and regular cycle counts. MRO inventory supports production but is not part of the finished product.

Software

Tools to plan, track, and optimize maintenance. Includes CMMS for work orders, EAM for lifecycle planning, and e-procurement tools to streamline purchasing.

Where time series databases fit in an MRO strategy

The shift toward predictive maintenance creates a data infrastructure challenge that traditional systems were never designed to handle. A modern manufacturing facility with thousands of IIoT sensors can generate billions of data points daily. This is time series data, and it requires specialized tools at scale.

Traditional relational databases and legacy data historians struggle with the volume, velocity, and query patterns of high-frequency sensor data. Time series databases are built for this workload. They are designed to ingest large volumes of timestamped data at high speed, compress it efficiently for long-term storage, and support the kinds of queries that maintenance and operations teams actually need: trend analysis over time windows, anomaly detection, and correlation across multiple sensor streams.

InfluxDB is one of the most widely adopted time series databases in industrial environments. It is built to handle the data patterns that MRO and predictive maintenance generate, and it fits into the maintenance technology stack in several important ways.

Real-time equipment monitoring: InfluxDB ingests data from PLCs, SCADA systems, and IIoT sensors via standard industrial protocols like MQTT, OPC UA, and Modbus through its Telegraf agent. This creates a live feed of equipment health data that maintenance teams can use to spot anomalies as they develop.
Historical context for predictive models: Effective predictive maintenance depends on having deep historical data to train machine learning models. InfluxDB stores years of sensor data in a compressed columnar format, making it practical and cost-effective to retain the historical depth that ML models need to identify failure patterns.
Bridging OT and IT systems: One of the persistent challenges in MRO is that operational technology and information technology often exist in separate silos. InfluxDB integrates with both sides of this divide, connecting industrial data sources at the edge with analytics tools, cloud platforms, and AI/ML pipelines on the IT side.
Edge-to-cloud flexibility: Not every facility has the same infrastructure. Some need on-premises data processing for latency or security reasons; others want cloud-based analytics. InfluxDB supports deployment at the edge, in private clouds, or in fully-managed cloud environments, allowing organizations to match their data architecture to their operational reality.

The practical impact is tangible. Seadrill has reported saving over $1.6 million in a single year by using InfluxDB as its time series database for equipment monitoring. Siemens Energy uses InfluxDB to monitor 23,000 battery modules across more than 70 sites, analyzing billions of sensor readings to prevent downtime and ensure quality.

For operations and maintenance teams evaluating their data infrastructure, the key question is whether their current systems can handle the data volumes that condition-based and predictive maintenance demand. If the answer is no, a time series database is the foundational layer that makes advanced maintenance strategies possible.

Common MRO challenges

Even well-intentioned MRO programs run into recurring problems.

Fragmented Spending

This is the most widespread issue. When every department or site purchases MRO supplies independently, organizations lose leverage with suppliers and have no visibility into total spend.

Reactive Maintenance Culture

This culture remains entrenched in many organizations. ABB’s Value of Reliability research found that two-thirds of companies experience unplanned downtime at least once per month, and a full third have not undertaken motor or drive modernization projects in the past two years, even though upgrading obsolete equipment can generate ROI in less than two years.

Poor Data Quality

Poor data quality undermines almost every MRO improvement effort. Incomplete asset records, mislabeled parts, and patchy work-order histories make it difficult to decide what to stock, when to maintain, and where to invest. This problem compounds as organizations try to implement predictive maintenance, which depends entirely on clean, structured, time-stamped data.

Excess and Obsolete Inventory

Excess and obsolete inventory tie up capital and warehouse space. Parts ordered for equipment that has since been retired, or spares stocked based on outdated failure rates, accumulate quietly until someone audits the stockroom.

How to improve an MRO strategy

There is no single playbook for MRO improvement, but a few principles apply broadly.

Start with visibility. Before you optimize anything, you need a clear picture of what you are spending, where your inventory sits, and how your assets are performing. Consolidating data from procurement, maintenance, and inventory systems is almost always the first step.
Classify assets by criticality. Not all equipment deserves the same level of attention. Focus preventive and predictive maintenance resources on the assets whose failure would cause the greatest impact on safety, production, or cost.
Consolidate suppliers and standardize parts. Reducing the number of MRO suppliers simplifies procurement, improves negotiating leverage, and makes it easier to manage inventory. Standardizing on common parts across similar equipment reduces the total number of SKUs you need to carry.
Shift from reactive to proactive maintenance. This is a long-term cultural change, not a one-time project. Start with the highest-criticality assets, prove the value with condition monitoring and predictive analytics, and then scale. Organizations that make this transition consistently report dramatic reductions in both downtime and total maintenance cost.
Invest in the right data infrastructure. Advanced maintenance strategies are only as good as the data infrastructure behind them. This means CMMS/EAM software for work order management, time series databases for high-frequency sensor data, and integration layers that connect these systems so that insights flow from the sensor to the decision-maker without friction.
Measure what matters. Track metrics that connect MRO performance to business outcomes: planned vs. unplanned maintenance ratio, spare parts availability, mean time between failures (MTBF), overall equipment effectiveness (OEE), and maintenance cost as a percentage of asset replacement value.

Wrapping up

MRO may not be the most glamorous line item in an operating budget, but it is one of the most consequential. The organizations that treat maintenance, repair, and operations as a strategic function consistently outperform those that don’t. As sensor technology gets cheaper, predictive analytics gets smarter, and the data infrastructure to support them becomes more accessible, the gap between reactive and proactive organizations will only widen. The best time to invest in your MRO strategy was five years ago. The second-best time is now.

MRO FAQs

What does MRO stand for?

MRO most commonly stands for maintenance, repair, and operations—the activities, supplies, and services that keep equipment and facilities running. In aviation and heavy industry, MRO can also stand for maintenance, repair, and overhaul, where "overhaul" refers to the complete teardown, inspection, and rebuild of a component or system to original specifications. Both meanings describe the same core concept: sustaining operational readiness of physical assets.

What is MRO in business?

In a business context, MRO refers to all indirect spending related to keeping operations running. This includes everything from preventive maintenance schedules and spare parts to safety equipment, cleaning supplies, and facility consumables. MRO sits outside of direct production costs but has a significant impact on uptime, safety, and total operating expense.

What is the difference between MRO inventory and production inventory?

Production inventory consists of raw materials and components that become part of the finished product. MRO inventory includes spare parts, tools, consumables, and supplies used to maintain equipment and facilities; items that support production but never appear in the final product. Both require management, but they serve different purposes and are often handled by different teams with different procurement strategies.

What is MRO in manufacturing?

In manufacturing, MRO covers the indirect materials (lubricants, filters, PPE, tools, electrical components) and maintenance activities (inspections, repairs, preventive maintenance) required to keep production equipment operational. It is one of the largest categories of indirect spending in most manufacturing organizations.

What is MRO in aviation?

In aviation, MRO stands for maintenance, repair, and overhaul. It is a heavily regulated segment that includes line maintenance, airframe and engine maintenance, component repair, and full overhauls of aircraft systems. Aviation MRO is essential for airworthiness certification and passenger safety, and it is governed by regulatory bodies like the FAA and EASA.

What are MRO supplies?

MRO supplies are the materials purchased to support maintenance and operational activities. Common examples include spare parts, fasteners, lubricants, hand tools, safety gear, cleaning products, electrical components, and facility consumables like light bulbs and HVAC filters. These items are consumed during the maintenance process rather than incorporated into a finished product.

Why is MRO important?

MRO directly affects equipment uptime, workplace safety, regulatory compliance, and operating costs. Unplanned downtime alone costs U.S. manufacturers an estimated $50 billion per year. Organizations that manage MRO effectively experience fewer breakdowns, lower total maintenance costs, longer asset lifespans, and better safety records. As maintenance strategies evolve from reactive to predictive, the strategic importance of MRO continues to grow.

What is the difference between preventive and predictive maintenance?

Preventive maintenance follows a fixed schedule. For example, replacing a filter every 90 days regardless of its condition. Predictive maintenance uses real-time data from sensors to forecast when maintenance is actually needed, based on the condition and performance trends of the equipment. Predictive approaches reduce both unnecessary maintenance and unexpected failures, but they require investment in sensors, data infrastructure, and analytics tools.

What is a CMMS and how does it relate to MRO?

A CMMS (computerized maintenance management system) is software used to schedule, track, and document maintenance activities. It is one of the core tools in an MRO program, helping teams manage work orders, track asset history, plan preventive maintenance schedules, and monitor spare parts inventory. More advanced platforms (often called EAM, or enterprise asset management systems) add lifecycle planning, capital project tracking, and integration with other enterprise systems.

Telegraf Enterprise Beta is Now Available: Centralized Control for Telegraf at Scale

Scott Anderson (InfluxData) — Thu, 26 Mar 2026 07:30:00 +0000

Telegraf is incredibly good at what it does: collecting metrics, logs, and events from just about anywhere and sending them wherever you need. But once Telegraf becomes part of your production telemetry pipeline, spread across environments, teams, regions, and edge locations, the hard part isn’t installing agents; it’s operating them.

Configs drift. “Temporary” overrides linger. Rolling out changes across hundreds (or thousands) of agents becomes a careful, manual process. And when something breaks, the first question is rarely about the data—it’s about the fleet:

which configuration is running where, and is every agent healthy?

That’s the problem Telegraf Enterprise is built to solve.

Today, we’re opening the Telegraf Enterprise beta to the broader Telegraf community so you can help us validate the product where it matters most: at scale.

What is Telegraf Enterprise?

Telegraf Enterprise is a commercial offering for organizations running Telegraf at scale and needing centralized management, governance, and support. It brings together two key components:

Telegraf Controller: A control plane (UI + API) that centralizes Telegraf configuration management and fleet health visibility.
Telegraf Enterprise Support: Official support for Telegraf Controller and official Telegraf plugins, designed for teams that need dependable response times and expert guidance.

It’s built for real-world, large-scale agent deployments, where Telegraf isn’t a tool you occasionally touch, but a platform you rely on.

Meet Telegraf Controller: your Telegraf control plane

At the heart of Telegraf Enterprise is Telegraf Controller, which centralizes two things teams struggle with most at scale:

Configuration Management That Doesn’t Collapse Under Growth

Telegraf Controller helps you create and manage configurations to support consistency across environments while still allowing necessary variation. Core capabilities include:

Centralized configuration creation and editing
Templates and parameterization to reuse configs safely
Label-based organization (so fleets don’t devolve into a long list of “agent-123”)
Bulk operations for fleet-wide changes
Environment variable and parameter management
Plugin metadata visibility to simplify config authoring and maintenance

Fleet-Wide Health Visibility

Telegraf Controller gives you a single view into the overall status of your agent deployments, so you can understand:

Which agents are reporting as expected
Where health issues are clustering
What changed recently, and what might be correlated

In other words, you don’t just manage Telegraf. You operate it.

Designed to fit your telemetry stack

Telegraf Enterprise is designed to work with the way teams actually deploy Telegraf.

It does not require InfluxDB. You can use the Telegraf Controller regardless of where your telemetry data is going.
Configuration delivery follows a pull-based model, where agents fetch configuration over HTTP. This keeps change management predictable and compatible with locked-down environments.
It’s built to support hundreds to thousands of agents, with production-grade storage options and a modern UI + API architecture for automation.

Why we’re running this beta

This beta is open to any Telegraf user who wants to test-drive Telegraf Controller.

The focus of the beta is simple:

Test Telegraf Controller at scale: We want to validate how well Telegraf Controller holds up when you connect real fleets—hundreds or thousands of agents—with real operational behaviors.
Gather feedback from the community: We’re intentionally inviting community input early, while we’re still shaping the GA experience. What workflows are missing? What’s confusing? What would make this tool indispensable in your environment?

At this stage, your feedback directly influences what Telegraf Enterprise becomes.

Enterprise support that matches production expectations

Operating telemetry pipelines is a production responsibility, and when Telegraf is part of that pipeline, you need support that understands the stakes.

Telegraf Enterprise includes support designed for teams that need:

Clear expectations for response and escalation
Coverage for Telegraf Controller and official Telegraf plugins
Help diagnosing issues and reducing operational risk as fleets grow

This is especially valuable when Telegraf is deployed across multiple teams, environments, or customer sites, where operational consistency matters as much as collection capability.

Who is Telegraf Enterprise for?

Telegraf Enterprise is built for organizations that manage Telegraf fleets at a meaningful scale, including:

Platform engineering and SRE teams
DevOps organizations operating across multi-cloud / hybrid / edge
Managed service providers delivering telemetry as a service
Compliance-sensitive teams that need standardized configurations and governance

If you’re running a small number of agents and are comfortable managing configs manually, you may not need Telegraf Enterprise today. But if Telegraf is everywhere—and your team is responsible for keeping it reliable—centralized control quickly becomes less “nice to have” and more “how did we operate without this?”

Packaging: free and enterprise options

Telegraf Controller

A free tier is available for teams that want centralized configuration management and visibility with pre-defined limits.

Telegraf Enterprise

For teams operating Telegraf as critical infrastructure, Telegraf Enterprise includes the Telegraf Controller packaged with enterprise support.

The key difference: the Telegraf Enterprise is built for scale and operational reliability, with support and capabilities aligned to production fleet management.

Getting started with Telegraf Controller

Telegraf Enterprise is designed for teams operating Telegraf as a core part of production telemetry pipelines. If Telegraf is already how you collect metrics, logs, and events across your infrastructure, Telegraf Controller is the missing piece that helps you operate that collection layer like a platform—not a pile of configs.

To join the beta, click here to opt in. Please share your feedback in-app with the feedback button or our slack channel #telegraf-enterprise-beta.

Join the beta, push it hard, share your use case, and what makes your workflow easier!

Unifying Telemetry in Battery Energy Storage Systems

Allyson Boate (InfluxData) — Thu, 19 Mar 2026 08:00:00 +0000

Battery energy storage systems (BESS) play a critical role in modern energy infrastructure. Utilities rely on these systems to balance renewable generation, stabilize grid operations, and respond to changing electricity demand. As deployments scale in size and complexity, operators require continuous insight into battery health, system performance, and grid interaction. Operators rely on telemetry generated across several operational platforms. Battery management systems monitor cell behavior, power conversion systems, and regulate energy flow, while plant control platforms track facility status. Energy management software and environmental sensors provide additional context about facility conditions.

In many deployments, however, this information remains scattered across separate monitoring environments. Operators often move between multiple dashboards to understand activity across a single facility. Many BESS operators are now adopting unified telemetry platforms that consolidate operational signals and create a clearer operational view of system behavior.

The operational reality of modern BESS systems

A battery energy storage facility is not a single system but a collection of specialized subsystems that manage energy storage, power conversion, and grid interaction. Each subsystem monitors a different aspect of facility performance and generates operational signals that help operators understand how the system behaves.

Several platforms produce these signals. Battery Management Systems (BMS) track cell-level conditions such as voltage, temperature, and state of charge to protect battery health. Power Conversion Systems (PCS), typically implemented through inverters, regulate how electricity flows between the battery and the grid.

Plant-level monitoring runs through SCADA platforms, which provide alarms, system status, and operational controls. Energy Management Systems (EMS) determine when energy should be stored or dispatched based on grid signals and market conditions, while environmental sensors monitor external factors such as ambient temperature.

Together, these systems create a continuous operational record of facility performance, but the resulting information does not always exist in a shared environment.

The fragmented reality of BESS telemetry

In most battery energy storage deployments, operational data originates from multiple independent platforms, as described above. This fragmentation reflects the modular design and deployment of energy storage facilities. Battery systems, power conversion equipment, and plant control platforms are frequently delivered by different vendors, each with its own software, data models, and monitoring tools.

Because these platforms monitor individual components rather than the entire facility, data is rarely consolidated automatically. Operators often rely on multiple dashboards to understand activity across a single storage site. Correlating events between subsystems may require switching between tools and manually comparing timestamps or operational signals.

The result? Operators have access to large volumes of operational information but lack a unified view of the facility as a whole. When events occur across multiple subsystems, understanding how those signals relate to one another requires time and effort.

Operational cost of data silos

Even small issues can require significant labor to diagnose. The data silos created by ala carte technologies prevent engineers from seeing how signals across the storage system relate.For example, a thermal anomaly—an unexpected rise in battery temperature—may require operators to review battery readings, compare inverter load behavior, and examine environmental conditions. Without a unified view of these signals, determining the cause can take time.

These delays affect both system reliability and financial performance. If operators cannot quickly determine why system capacity dropped or alarms triggered, dispatch readiness may be affected during critical market windows. Over time, slower investigations and delayed anomaly detection can lead to reduced system availability, higher operational overhead, and missed revenue opportunities.

What unified telemetry actually means

Unified telemetry consolidates operational signals from across the storage system into a shared data environment. Instead of storing data separately within subsystem platforms, telemetry from across the facility enters a common dataset.

In this environment, operational signals are stored as time-series data, or measurements organized by timestamp, allowing signals from different subsystems to be synchronized and analyzed together.

This shared dataset allows engineers to correlate signals that were previously isolated. Battery temperature trends can be examined alongside inverter load behavior, dispatch signals, and environmental conditions to better understand system performance. Instead of switching between monitoring platforms, operators can observe how signals across subsystems evolve together within a unified operational timeline.

How unified telemetry works

In many deployments, telemetry aggregation begins at the edge of the facility. Edge collectors connect to operational systems such as the BMS, PCS, SCADA platform, EMS and environmental sensors using industrial protocols such as Modbus, OPC-UA, or CANbus. These collectors ingest operational signals and convert them into structured telemetry streams.

From there, the data flows through streaming pipelines into centralized platforms. These pipelines handle ingestion, buffering, and transport of high-frequency signals so information from across the facility can be processed as a continuous operational stream.

Time series databases store and index this telemetry by timestamp, allowing engineers to query system behavior over time. Organizing operational signals this way enables teams to correlate events across subsystems, analyze performance trends, and investigate anomalies.

Because signals from different systems exist in the same time-aligned dataset, engineers can examine battery performance, inverter activity, dispatch signals, and environmental conditions together. This enables faster incident investigation and supports advanced analysis such as anomaly detection and predictive maintenance.

Operational impact

Unified telemetry changes how energy storage facilities are operated and how organizations manage risk, reliability, and revenue. When signals from battery systems, power electronics, and plant controls are analyzed together, operators gain a comprehensive view of facility behavior rather than having to reconstruct events across multiple monitoring platforms.

This visibility allows teams to detect anomalies earlier and respond to operational issues before they escalate. Faster diagnosis reduces downtime and helps maintain system availability during critical dispatch windows. In energy markets, maintaining dispatch readiness helps protect revenue during high-value trading periods.

ju:niz Energy Deployment

ju:niz Energy operates large-scale battery storage systems that provide grid services and trading flexibility in energy markets. Their systems collect thousands of data points per second on battery health, temperature, climate conditions, and system performance.

To manage this telemetry, ju:niz built a centralized monitoring architecture using Telegraf, Modbus, MQTT, Grafana, Docker, AWS, and InfluxDB. Operational signals from battery systems stream into a centralized time series platform, giving engineers a unified view of system behavior and eliminating the need for legacy Python monitoring scripts.

This architecture enables the ju:niz team to analyze battery telemetry in real-time, improve alerting accuracy, and support predictive maintenance strategies across their storage infrastructure.To see how ju:niz implemented unified telemetry for its operations, read the full case study or watch the webinar.

The bottom line

Battery energy storage systems generate telemetry across multiple operational platforms, but when that data remains fragmented, operators struggle to understand how the system behaves as a whole. Unified telemetry solves this by bringing operational signals into a shared, time-aligned dataset. As BESS deployments scale, this capability will become foundational for operating energy storage systems reliably, efficiently, and profitably.

Ready to build a unified telemetry architecture? Get started with a free download of InfluxDB 3 Core OSS or a trial of InfluxDB 3 Enterprise.

A New Scale Tier for Time Series on Amazon Timestream for InfluxDB

Pat Walsh (InfluxData) — Mon, 16 Mar 2026 08:00:00 +0000

When we first announced the availability of InfluxDB 3 Core and Enterprise on Amazon Timestream for InfluxDB last year, we set a new standard for managed time series on AWS. We gave developers a simple way to harness high performance at scale while removing the burden of infrastructure management.

But as our customers have taught us, “at scale” is a moving target. Across Industrial IoT, physical AI, and real-time observability, data is growing in both volume and resolution. When you move from minute-by-minute polling to sub-millisecond, high-fidelity telemetry, the pressure on the underlying database compounds. To stay ahead of that curve, developers need a platform that scales as fast as their workloads.

Today, we’re delivering that by expanding InfluxDB 3 on Amazon Timestream for InfluxDB to support expanding clusters up to 15 nodes. We’re also introducing a seamless migration path from InfluxDB 3 Core to InfluxDB 3 Enterprise, allowing teams to unlock this massive performance tier without friction, risk of a manual architectural overhaul, or any data loss.

Scaling for the mission-critical

At InfluxData, we’re seeing time series expand from infrastructure monitoring to the foundation for autonomous systems. In high-stakes environments like power grid management or autonomous vehicle navigation, increased latency is a significant operational risk rather than just a performance metric.

Previously, AWS Timestream’s support of InfluxDB 3 was focused on smaller, highly efficient configurations. By expanding to 15 nodes, we are providing major upgrades across three important areas:

Query concurrency: More nodes mean more hands on deck to process complex, concurrent queries. Large teams can now run heavy analytical workloads without impacting real-time dashboards or critical alerts.
Massive throughput: With a larger cluster, you can ingest millions of data points per second across hundreds of millions of unique series, maintaining real-time query performance.
Workload isolation and optimization: These expanded clusters enable true functional isolation between ingestion, queries, and compaction. This allows granular performance tuning optimized for your most demanding workloads.

Architected for enterprise demand

This new 15-node option is available for InfluxDB 3 Enterprise and is designed for organizations that require high availability, enhanced security, and the power to maintain high ingestion and real-time query performance across high-resolution, high-velocity datasets. InfluxDB 3 Core will continue to operate in single-node deployments.

By leveraging AWS infrastructure, you can spin up these expanded clusters in minutes directly from the AWS Console. With our new seamless migration capabilities, you can transition your existing Core workloads to Enterprise clusters with a single click. This ensures that as your data grows (from a few local sensors to a global fleet of devices), your database never becomes the bottleneck, and your team never has to worry about the downtime typically associated with a migration. These larger clusters are available today in all AWS regions where Amazon Timestream for InfluxDB is available, ensuring you can deploy and optimize mission-critical time series infrastructure wherever your data lives.

The foundation for physical AI

Our partnership with AWS is about meeting developers where they build. By integrating with services like AWS Lambda, SageMaker, and Kinesis, we’ve simplified the path from high-volume streams into Physical AI. This is the frontier where intelligence moves from the digital realm into the physical world.

Time series is the heartbeat of this transition, fueling a two-part lifecycle:

Training: Using massive volumes of historical data to establish baselines and “normal” patterns.
Inference: Streaming real-time data against those models to trigger automated, deterministic actions.

What makes our partnership with AWS unique is that we support both sides of this loop. With up to 15 nodes at your disposal, InfluxDB 3 has the headroom to act as a distributed inference engine, running predictive maintenance and anomaly detection against your data. This eliminates the latency tax of moving massive datasets between layers, ensuring that whether you are managing a robotic fleet or a smart grid, your autonomous systems can perceive and react with real-time precision.

What’s next?

The future of time series is about speed, precision, and scale. With today’s announcement, we’re handing you the keys to all three. By removing the barriers between single-node efficiency and enterprise-grade performance, we’re making it easier than ever to evolve your architecture as fast as your data grows.

We’re excited to see what the community builds with this new level of power. If you’re ready to scale your real-time workloads, head over to the AWS Console and start building.

What is Industry 4.0? Everything You Need to Know in 2026

Company (InfluxData) — Fri, 13 Mar 2026 08:00:00 +0000

Industry 4.0 is the term used to describe the fourth industrial revolution, a name given to the integration of physical and digital systems, which includes the internet of things (IoT) and artificial intelligence that are transforming a huge number of industries.

At a high level, its goal is to create an efficient, automated process for creating products or services that can be adapted quickly and efficiently to changing customer needs.

Industry 4.0 also includes concepts such as cloud computing, big data analytics, and machine learning to enable smarter production processes.

By using sensors and automation technology, manufacturers can collect real-time data on their machines and operations, which can be analyzed to make more informed decisions about how best to manage resources, optimize production lines, and reduce costs.

Industry 4.0 is leading manufacturers away from the traditional linear, push-based approach to production toward a new data-driven, customer-centric model. This “smart” manufacturing can help businesses remain competitive and stay ahead of the curve in terms of production capabilities, while also contributing to a more sustainable future.

The path to Industry 4.0

Let’s take a look at how we arrived at Industry 4.0 by looking to the past. This additional context will help give you a better understanding of why Industry 4.0 is important and why so many people think it is valuable to adopt these technologies.

First Industrial Revolution

The First Industrial Revolution, which took place in the late 18th and early 19th centuries, was characterized by the mechanization of production, the use of steam power, and the development of the factory system.

This revolution led to significant changes in manufacturing, transportation, and communication, and had a major impact on society and the economy.

Second Industrial Revolution

The Second Industrial Revolution took place in the late 19th and early 20th centuries. It was characterized by mass production of goods, the use of electricity, and the development of the assembly line.

Third Industrial Revolution

The Third Industrial Revolution, also known as the Digital Revolution, took place in the late 20th and early 21st centuries and was characterized by the adoption of computers and automation in manufacturing and other industries.

Fourth Industrial Revolution

Industry 4.0, also known as the Fourth Industrial Revolution, is the current trend of automation and data exchange in manufacturing technologies, including developments in artificial intelligence, the internet of things (IoT), and cyber-physical systems.

It’s seen as the fourth major revolution in manufacturing, following the mechanization of production in the First Industrial Revolution, the mass production of the Second Industrial Revolution, and the introduction of computers and automation in the Third Industrial Revolution.

Industry 4.0 key concepts and principles

Interoperability

Interoperability is a fundamental concept in Industry 4.0, emphasizing seamless communication and data exchange among systems, devices, and software platforms within an industrial environment.

As Industry 4.0 relies heavily on integrating diverse technologies such as IoT, AI, and cloud computing, ensuring these components work effectively together is crucial to realizing the full potential of a connected, intelligent manufacturing ecosystem.

Interoperability enables businesses to break down silos, streamline processes, and make better-informed decisions, ultimately leading to increased efficiency, productivity, and competitiveness.

To achieve interoperability, manufacturers must adopt standardized communication protocols, open architectures, and flexible data formats to facilitate a smooth flow of information across the entire production chain.

Virtualization

Virtualization is the creation of virtual representations of physical assets, processes, and systems within the industrial environment.

By using advanced technologies such as digital twins, simulation software, and augmented reality, virtualization enables manufacturers to test, analyze, and optimize their operations without impacting the actual production process.

Virtualization not only allows more efficient planning and decision making but also helps businesses identify potential bottlenecks or issues before they occur, resulting in reduced downtime, lower costs, and enhanced product quality.

At the same time, it promotes remote monitoring and control of industrial processes, allowing experts to collaborate and troubleshoot issues from any location, which improves overall operational efficiency.

Cyber-Physical Systems

Cyber-physical systems (CPS) are a core part of Industry 4.0, representing the seamless integration of computational and physical components. These systems enable real-time communication and data exchange between machines, humans, and digital networks, resulting in smarter, more efficient, and autonomous industrial processes.

Decentralization

Decentralization involves the shift towards distributed decision-making and autonomous control within industrial systems.

In the context of manufacturing, decentralization empowers machines, devices, and production units to make decisions and perform tasks independently, without centralized supervision or control.

This approach increases the agility and resilience of manufacturing operations and enables businesses to scale more effectively, as new components or devices can be seamlessly integrated into the existing network.

Modularity

Modularity, the ability to adjust production lines, processes, and equipment with minimal effort and downtime, is a key concept in Industry 4.0.

It emphasizes the importance of designing flexible, scalable, and adaptable systems that can be easily reconfigured or upgraded to meet changing market demands and technological advancements.

By embracing modularity, manufacturers can rapidly adapt to fluctuations in product demand, introduce new products, or incorporate emerging technologies, ensuring their operations remain agile and competitive.

Modularity also enables greater customization, as production lines can be adjusted to accommodate unique customer requirements or preferences.

What technologies are driving Industry 4.0?

Internet of Things

IoT is an important part of Industry 4.0, enabling businesses to optimize processes and become more efficient. With this technology, companies can deploy intelligent machines to automate processes and workflows, leading to higher accuracy and productivity.

IoT technology also makes it possible for machines and databases to communicate, allowing businesses to access real-time data. This improved data collection has enabled insights about productivity and efficiency, streamlining many processes in Industry 4.0.

Cloud Computing

Cloud computing enables new ways for organizations to develop agile digital operations. By using cloud computing, companies can reduce the time needed to deploy or upgrade applications and further benefit from scalability.

With cloud computing, manufacturers now have access to analytics data they did not previously have, enabling them to make informed, real-time decisions.

Edge Computing

Edge computing is the process of collecting and analyzing data at the edge of a network, closer to where it is generated. It’s at the opposite end of the spectrum from cloud computing, but it’s just as important for Industry 4.0 workloads.

This makes it ideal for applications that require real-time analytics, such as autonomous robotic systems and self-driving cars.

Edge computing also helps reduce network traffic by minimizing the need to send large amounts of data back and forth between devices and centralized data centers.

5G Networking

5G networks allow for faster communication and data transfer speeds, a huge factor in making Industry 4.0 viable. This ultimately makes the technology more accessible to businesses of all sizes and enables them to deploy IoT solutions at scale.

5G can enable companies to increase operational efficiency by supporting real-time decision-making and remote monitoring capabilities.

AI and Machine Learning

AI and machine learning are another key piece of making Industry 4.0 possible. Using AI, companies are able to automate processes, improve decision-making, and better analyze data.

Many industries are already using AI to increase efficiency, accelerate innovation, and reduce costs. In manufacturing, for example, AI can be used to optimize production lines, predict maintenance needs, and schedule resources more efficiently.

Cybersecurity

Collecting and analyzing more data is great, but it also opens up numerous potential vulnerabilities for businesses. No company wants to be in the news for leaking internal or customer data, or for not being able to function because critical infrastructure has been hacked.

Industry 4.0 requires sophisticated cybersecurity solutions that protect data at rest and in transit, detect malicious activity before it becomes a problem, and alert users when something is amiss. This can be accomplished through various measures such as encryption, intrusion detection systems, two-factor authentication (2FA), and network segmentation.

In addition to implementing security solutions, organizations should also develop a comprehensive cybersecurity strategy that covers personnel training and processes for responding to emergency situations. This way, businesses can be more prepared for any potential attacks or data breaches.

Digital Twins

Digital twins enable engineers to create virtual models of systems and processes that can be used to measure performance, anticipate variation, and even detect defects or dangers before they become issues in the physical world.

As a result of this technology’s high accuracy, digital twin simulations can substantially reduce design costs, improve operational efficiency and sustainability, enhance product quality, and promote workplace safety.

Furthermore, companies are leveraging the combination of digital twins’ advanced analytics capabilities and connected devices to optimize factory operations through remote commissioning, proactive maintenance, and streamlined troubleshooting.

Real-Time Data Analytics

Real-time analytics is an essential part of Industry 4.0, enabling businesses to monitor, analyze, and respond to operational and process changes with unprecedented speed and accuracy.

By utilizing IoT devices, sensors, and advanced analytics models, manufacturers can collect and process data in real time, allowing them to make data-driven decisions and adjustments on the fly.

3D Printing and Additive Manufacturing

3D printing and additive manufacturing are quickly becoming essential tools for businesses to maximize efficiency, reduce costs, and create complicated designs with ease.

For example, factories can print replacement parts on-site without having to call a supplier and wait for them to arrive. This means faster repairs and less downtime overall.

Additive manufacturing also allows companies to manufacture complex designs that were previously impossible with traditional manufacturing methods.

Robotics

In the context of Industry 4.0, robotics goes beyond traditional automation, incorporating advanced capabilities such as AI, machine learning, and sensor integration to create intelligent, adaptive, and versatile machines capable of performing complex tasks with precision and consistency.

This also includes collaborative robots, or “cobots,” which are designed to work alongside human operators, enhancing their capabilities and ensuring a safer, more ergonomic work environment. By using robotics, manufacturers can automate repetitive tasks, reduce human error, and reduce labor costs, while also enabling greater flexibility and customization in production.

Benefits of Industry 4.0

1. Improved productivity

One of the primary benefits of Industry 4.0 is improved productivity. Key 4.0 technologies, such as data analytics and machine learning, can be used to identify inefficiencies and optimize production processes.

Similarly, robotics and 3D printing can automate tasks, reducing the need for human labor and increasing manufacturing output.

2. Increased efficiency

By enabling smarter use of resources and more efficient processes, Industry 4.0 contributes significantly to reducing energy consumption, waste generation, and greenhouse gas emissions.

When companies adopt Industry 4.0 technologies, they can actively contribute to global sustainability goals while simultaneously improving their bottom line.

Predictive maintenance is a prime example. This proactive approach allows companies to monitor equipment performance in real-time, identify potential issues before they escalate, and schedule maintenance activities based on actual equipment conditions rather than fixed intervals.

Predictive maintenance minimizes unexpected downtime and costly repairs, extends equipment lifespan, reduces the need for frequent replacements, and reduces associated environmental impact. As an added bonus, equipment that is properly maintained also tends to run more efficiently in terms of power consumption and greenhouse gas emissions.

3. Improved quality

By identifying errors in collected sensor data, Industry 4.0 can also help improve product quality. Additionally, 3D printing can create prototypes that can be tested for quality before mass production begins.

4. Reduced costs

The implementation of Industry 4.0 technologies helps minimize expenses because these technologies can help improve productivity and efficiency, leading to reduced labor costs and waste.

5. Increased flexibility

Industry 4.0 helps to increase flexibility within manufacturing operations. Technologies such as 3D printing and robotics can be used to create customized products quickly and with minimal human labor.

The use of data analytics also helps companies respond to changes in customer demand, scaling production up or down when needed.

6. Enhanced safety

Thanks to advances such as robotics and machine learning, dangerous tasks can now be automated. This reduces the risk of worker injury and helps create a safer working environment.

7. More resilient supply chains

Adopting many Industry 4.0 technologies can help businesses strengthen their supply chains. By leveraging data analytics, businesses can monitor the production process in real time and detect small issues before they escalate into larger problems.

Plus, 3D printing and additive manufacturing can also be used to quickly produce replacement parts or components for machinery with little to no downtime. This helps companies maintain operations without disruption due to supply chain problems.

8. Improved customer experience

Industry 4.0 can help businesses improve their customer experience by providing insights into customer behaviors and preferences. Through data analysis, companies can identify areas where they need to focus their efforts in order to provide the best possible service or product.

Data can also help during the manufacturing process to help identify potential defects early, so customers don’t receive a faulty product.

Industry 4.0 challenges and risks

1. Implementation costs

Implementing Industry 4.0 technologies and practices can be expensive, particularly for smaller businesses. If a business doesn’t have the necessary financial resources to invest in these technologies, it may not see a return on the investment.

2. Cybersecurity risks

The integration of advanced technologies and the reliance on connected systems increase the risk of cybersecurity threats. Without robust cybersecurity measures in place, a business may be vulnerable to attacks, which can have serious consequences.

3. Culture challenges

Some businesses may be hesitant to adopt new technologies and practices due to concerns about costs and disruptions to their existing operations. If a business isn’t willing to adapt to new technologies and processes, it may struggle to compete with competitors that are more forward-thinking.

This can also apply to employees who aren’t familiar with new technologies and may be resistant to change, making it important to ensure that employees at all levels of the company understand how and why changes are being made.

Common Industry 4.0 use cases

1. Smart manufacturing

Smart manufacturing and smart factories are common Industry 4.0 use cases where adopting new technologies can improve productivity, make products more reliable, and keep workers safer.

Beyond the direct benefits to the company, smart manufacturing can benefit the environment by reducing waste and making production more efficient.

2. Agriculture

The advantages of incorporating Industry 4.0 in agriculture are substantial.

Precision farming techniques, powered by IoT sensors and data analytics, facilitate the targeted application of fertilizers, pesticides, and irrigation, reducing waste and minimizing environmental impact.

Robotics and autonomous machinery can also perform repetitive tasks, such as planting, harvesting, and monitoring, improving efficiency and freeing up valuable human resources.

Advanced data analysis also enables predictive modeling and forecasting, helping farmers make informed decisions on crop selection, planting schedules, and resource allocation.

3. Healthcare

By using IoT devices to collect health data, patients are able to get more personalized and effective healthcare. This can include everything from detecting emergency situations, such as a heart attack, to enabling the detection and mitigation of diseases before they become severe.

Robotics is also increasingly used during surgery to reduce human error and improve outcomes.

4. Supply chain management

Adopting Industry 4.0 technologies can enhance supply chain management by enabling better visibility, efficiency, and resilience.

Connecting components such as suppliers, manufacturers, distributors, and retailers, enables smoother information exchange, ensuring that all stakeholders have access to accurate and up-to-date data.

Predictive analytics and machine learning can help forecast demand patterns, optimize inventory levels, and identify potential disruptions, allowing supply chain managers to address issues and minimize risks.

Industry 4.0 tools

In this section, we’ll examine some tools useful for a variety of tasks involved in adopting industry 4.0 technology.

1. Data storage

Storing Industry 4.0 data at scale requires scalable, efficient solutions that can handle the high volume of data generated by interconnected devices and systems. Here are a few different options for storing your data:

2. Time series databases

Time series databases (TSDBs) are specifically designed to store time-stamped data from sensors and IoT devices. They offer high write and query performance, making them ideal for handling the high-frequency data typical of Industry 4.0 use cases. An example of a TSDB is InfluxDB.

3. Data historians

Data historians are specialized databases for storing and retrieving historical process data from industrial systems. They are optimized for handling time series data and offer capabilities like data compression, aggregation, and real-time querying. An example of a data historian is OSI PI.

4. Columnar databases

Columnar databases store data in columns rather than rows, which is well-suited for analytics and processing large datasets and is often used as a data warehouse. Columnar databases offer high query performance and data compression, making them suitable for storing and analyzing the vast amounts of structured data generated by Industry 4.0 systems.

5. Communication protocols

Several communication protocols are well-suited for Industry 4.0 systems, providing efficient and reliable data transfer between interconnected devices, machines, and software platforms. Here are some good options for communication protocols in Industry 4.0:

6. MQTT

MQTT is a lightweight, publish-subscribe messaging protocol designed for low-bandwidth, high-latency, and unreliable networks. Its low overhead and minimal resource requirements make it ideal for IoT devices and Industry 4.0 applications.

MQTT is widely used to connect sensors, actuators, and other devices to cloud platforms, enabling efficient data exchange and remote monitoring.

7. OPC Unified Architecture (OPC UA)

OPC UA is a platform-independent, service-oriented architecture developed specifically for industrial automation and communication. It provides secure and reliable data exchange between devices, machines, and software applications, regardless of the underlying platform or programming language.

OPC UA supports a wide range of data types and features with built-in security mechanisms, making it a popular choice for Industry 4.0 systems.

8. Advanced Message Queuing Protocol (AMQP)

AMQP is an open standard, application-layer protocol for message-oriented middleware. It supports flexible messaging patterns and offers reliable, secure communication between devices and applications. AMQP is well-suited to scenarios that require complex routing and guaranteed message delivery, making it a good fit for many Industry 4.0 applications.

Data Collection and Integration

One of the big challenges for Industry 4.0 is collecting data from a variety of devices that may communicate over different protocols, then sending it to various tools for storage and analysis. Let’s take a look at some options that make collecting and integrating data easier:

1. Node-RED

Node-RED is an open-source, flow-based programming tool for wiring together devices, APIs, and online services. It provides a browser-based visual interface for designing and deploying data flows, making it easy to connect and integrate various data sources, such as IoT devices, industrial sensors, and web services.

With a large library of prebuilt nodes and support for custom nodes, Node-RED allows users to build complex data pipelines and perform data transformations with minimal coding effort.

2. Telegraf

Telegraf is an open source, plugin-driven server agent for collecting and reporting metrics from different data sources. Telegraf supports a wide range of input, output, and processing plugins, allowing it to gather and transmit data from various devices, systems, and APIs to different storage platforms.

Its flexibility and extensibility make it suitable for Industry 4.0 applications, where diverse data sources are common.

3. Apache NiFi

Apache NiFi is an open source, web-based data integration tool for designing, deploying, and managing data flows. It offers a visual interface for designing data pipelines and supports a wide range of data sources, processors, and sinks.

NiFi is particularly well-suited to use cases that require complex data routing, transformation, and enrichment. With built-in security features and support for data provenance, NiFi ensures data integrity and traceability in Industry 4.0 environments.

Industry 4.0 best practices

Moving towards Industry 4.0 is a major endeavor for existing businesses and involves all areas of a business to work properly. In this section, let’s explore some best practices that can help you avoid major pitfalls that could hurt your business.

1. Have a clear strategy and goals

Above all else, you need a clear understanding of how adopting these new technologies will help achieve your business goals. If you can’t actually find concrete ways that this will help your business, don’t blindly invest resources in them. Some potential things to identify:

Specific technologies that will be used
Which processes could be automated
Metrics to measure success
Cybersecurity focus

The integration of advanced technologies and the reliance on connected systems increase the risk of cybersecurity threats. Implement robust cybersecurity measures to protect against these threats from day one, so you don’t regret it later on.

2. Collaboration

Industry 4.0 technologies often involve integrating systems and processes across different organizations. It’s important to collaborate with suppliers and partners to ensure that these systems and processes are integrated effectively.

3. Track results and iterate

Establish metrics before starting so you can measure progress against expected results. Based on progress, you need to be willing and able to change your strategy if necessary.

FAQs

What are the origins of Industry 4.0?

Industry 4.0 as a concept dates back to 2006, when the German government laid out a plan to maintain its manufacturing dominance in a paper that looked into the future of manufacturing and how companies would be impacted and need to adapt to emerging technologies. The concept was further refined in 2010 when the German Cabinet laid out their High-Tech Strategy 2020 plan, which defined five priorities that would be used to direct billions of dollars in government investment.

How are digital transformation and Industry 4.0 related?

Digital transformation and Industry 4.0 are often used interchangeably, but it's crucial to understand their unique characteristics and how they relate to each other. While both concepts involve adopting advanced technologies to improve business operations, Industry 4.0 specifically focuses on the manufacturing sector, whereas digital transformation encompasses a broader range of industries and applications. Digital transformation is the process of integrating digital technologies across a business's customer service, marketing, supply chain management, and internal operations. The goal of digital transformation is to optimize processes, enhance efficiency, and create new business models that drive growth and competitiveness. This transformation is achieved through the implementation of technologies such as cloud computing, data analytics, artificial intelligence, and IoT. Industry 4.0, on the other hand, is a subset of digital transformation that targets the manufacturing industry. It is often referred to as the Fourth Industrial Revolution, representing a new era of intelligent, connected, and autonomous manufacturing systems. Industry 4.0 leverages technologies like IoT, advanced analytics, robotics, and additive manufacturing to optimize production processes, improve product quality, and increase overall efficiency. Despite their differences, digital transformation and Industry 4.0 are closely related, as both aim to drive innovation and create value through the adoption of advanced technologies. In fact, Industry 4.0 can be considered a specific application of digital transformation within the manufacturing sector. As companies embark on their digital transformation journeys, embracing Industry 4.0 principles can provide a solid foundation for growth and success in manufacturing.

What is IT/OT convergence?

Businesses have traditionally been siloed between information technology (IT) and operational technology (OT). But in recent years, these worlds have started to merge in a process commonly referred to as IT/OT convergence. Better collaboration between IT and OT can add tremendous value to any business by providing greater visibility across the organization, improved data analysis capabilities, fewer manual processes, and a faster response to customer needs. By leveraging both sets of technologies, businesses can gain unprecedented control over their operations. IT/OT convergence involves integrating hardware, software, and networks traditionally used in OT with those used in IT. This integration synchronizes the two disconnected systems, allowing them to exchange data and information. For example, an IT system can enable operators to access real-time operational data from OT systems, such as sensors and actuators.

What is Industry 5.0?

Industry 5.0 is a term used to describe the next phase of the Fourth Industrial Revolution, characterized by the integration of advanced technologies such as AI, IoT, and quantum computing into manufacturing and other industries. There isn't a universally accepted definition of Industry 5.0, and the concept is still evolving. However, it's generally seen as a continuation of the trend towards increased automation and data exchange that began with Industry 4.0, with a focus on even more advanced technologies and their integration across sectors. One key difference between Industry 4.0 and Industry 5.0 is the focus on sustainability and social responsibility. Industry 5.0 is expected to involve the development of technologies that are more environmentally friendly and that promote social equity. This could include using renewable energy sources and developing technologies to reduce waste and pollution. Overall, the main difference between Industry 4.0 and Industry 5.0 is the level of technological advancement. Industry 5.0 involves the integration of even more advanced technologies, such as quantum computing, which have the potential to significantly impact and transform various industries.

When Your Plant Talks Back: Conversational AI with InfluxDB 3

Suyash Joshi (InfluxData) — Tue, 10 Mar 2026 08:00:00 +0000

No one wants to stare at a plant and guess if it needs water. It’s much easier if the plant can say, “I’m thirsty.” A few years ago, we built Plant Buddy using InfluxDB Cloud 2.0. The linked article is still a great guide for cloud-first IoT prototyping as it shows how quickly you can connect devices, store time series data, and build dashboards in the cloud with the previous version of InfluxDB.

But this time, the goal was different. Instead of sending soil moisture data to the cloud, the entire system runs locally using the latest InfluxDB 3 Core, similar to a modern industrial setup powered by LLM for a natural conversational interaction.

The architecture: the “factory” at home

In real factories, raw PLC data is captured at the edge, often using MQTT and a local database. That same architecture now powers PlantBuddy v3 with the following setup.

Edge Device (ESP32 / Arduino): Works like a small PLC. It reads soil moisture and publishes the plant’s state to the network without knowing anything about the database.
Soil Moisture Sensor (Analog): Outputs an analog signal based on soil moisture. The microcontroller converts it to digital using its built-in ADC.
Message Bus (Mosquitto MQTT): Handles publish/subscribe communication. The Arduino publishes data to the broker (running locally), and Telegraf subscribes to forward data to the database.
Database (InfluxDB 3 Core): Runs locally in Docker as a high-performance time series database storing all sensor readings.
User Interface (Claude + MCP): Enables natural language queries. Instead of writing SQL, questions about plant health can be asked conversationally.

1. Collecting & Sending Data from the Edge

To make this scalable, I treat the sensor data like an industrial payload. It’s not just a number; it’s a structured JSON object containing the ID, raw metrics, and a pre-calculated status flag.

The Arduino Payload

{ 
"id": "pothos_01",    // Device identifier (like a PLC tag) 
"raw": 715,  		// Raw ADC value (0-1023) 
"pct": 19,  		// Calculated moisture percentage 
"stat": "DRY_ALERT"   // Pre-computed status 
}

Why compute status at the edge? In factories, PLCs make local decisions (e.g., stop motor, trigger alarm). Here, the Arduino determines “DRY_ALERT” so the database can trigger alerts without recalculating thresholds.

2. The Ingest Pipeline

Below are two approaches to send plant data to InfluxDB. In this project, I went with MQTT and Telegraf, which are more standard for an industrial setup.

Telegraf acts as the gateway, subscribing to the MQTT broker and translating the JSON into InfluxDB Line Protocol. This configuration is identical to what you’d see in a manufacturing plant monitoring vibration sensors.

# telegraf.conf - Complete Working Example
[agent]
  interval = "10s"
  flush_interval = "10s"

[[inputs.mqtt_consumer]]
  servers = ["tcp://127.0.0.1:1883"]
  topics = ["home/livingroom/plant/moisture"]
  data_format = "json"

  # Tags become indexed dimensions (fast filtering)
  tag_keys = ["id", "stat"]

  # Fields become measured values
  json_string_fields = ["raw", "pct"]

[[outputs.influxdb_v2]]
  urls = ["http://127.0.0.1:8181"]
  token = "$INFLUX_TOKEN"
  organization = "my-org"
  bucket = "plant_data"

Note: If Telegraf runs in Docker, use host.docker.internal:8181 to reach the database.

3. Time Series Database: InfluxDB 3 (Docker Container)

InfluxDB 3 Core runs locally in Docker as the time series database. It stores soil moisture readings and enables real-time analytics, all without depending on external cloud connectivity.

# Create persistent storage 
mkdir -p ~/influxdb3-data

# Run InfluxDB 3 Core with proper configuration
docker run --rm -p 8181:8181 \
  -v $PWD/data:/var/lib/influxdb3/data \
  -v $PWD/plugins:/var/lib/influxdb3/plugins \
  influxdb:3-core influxdb3 serve \
    --node-id=my-node-0 \
    --object-store=file \
    --data-dir=/var/lib/influxdb3/data \
    --plugin-dir=/var/lib/influxdb3/plugins

4. The “AI” Interface: InfluxDB MCP & Claude

Instead of writing SQL queries or building dashboards, the system connects an LLM to InfluxDB through the Model Context Protocol (MCP). I’ve written another blog post on how to connect InfluxDB 3 to MCP, which you can find here.

Now the question isn’t: “What’s the SQL query for average soil moisture over the last 24 hours?”

It becomes: “Has the plant been dry today?”

The LLM generates the correct SQL under the hood. If needed, the generated query can be inspected. This makes time series analytics accessible through conversation.

claude_desktop_config.json

{
  "mcpServers": {
    "influxdb": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "--interactive",
        "--add-host=host.docker.internal:host-gateway",
        "--env",
        "INFLUX_DB_PRODUCT_TYPE",
        "--env",
        "INFLUX_DB_INSTANCE_URL",
        "--env",
        "INFLUX_DB_TOKEN",
        "influxdata/influxdb3-mcp-server"
      ],
      "env": {
        "INFLUX_DB_PRODUCT_TYPE": "core",
        "INFLUX_DB_INSTANCE_URL": "http://host.docker.internal:8181",
        "INFLUX_DB_TOKEN": "YOUR_RESOURCE_TOKEN"
      }
    }
  }
}

The Result:

What’s next

In the next post, we will upgrade this Plant Buddy project to do more than passively monitor. New features will include:

A water pump, motor, and small display.
Automatic watering when the plant enters DRY_ALERT.
An extended system with light and temperature sensors to determine how placement of the potted plant affects its health, especially during trips when no one is home.

Try to build one yourself with InfluxDB 3! We would love to hear your questions/comments in our community forum, Slack, or Discord.

InfluxData Blog - Developer

Less Friction, More Control: Here's What Shipped in Q1

Telegraf Controller enters beta

InfluxDB 3.9 adds more operational control

Processing Engine updates make InfluxDB 3 easier to operationalize

Better visibility for Cloud Dedicated customers

InfluxDB Enterprise 1.12.3 delivers efficiency gains for v1 environments

Amazon Timestream for InfluxDB adds a new scale tier and simple upgrade path

Looking ahead

New Plugins, Faster Writes, and Easier Configuration: What’s New with the InfluxDB 3 Processing Engine

A quick refresher: Processing Engine plugins

New plugins: data ingestion, import, and validation

InfluxDB Import Plugin

Data subscription plugins: MQTT, Kafka, and AMQP

OPC UA Plugin

Schema Validator Plugin

Processing Engine general improvements

Easier plugin configuration in Explorer

What’s next

Try them out

What’s New in InfluxDB 3.9: More Operational Control and a New Performance Preview

Operational maturity and system transparency

Now in beta: A new performance preview

Get started and share your feedback:

What is MRO? Maintenance, Repair, and Operations Explained

What is MRO?

Common MRO Supplies and Activities

Why MRO matters

Maintenance strategies: preventive, predictive, planned, and condition-based

Preventive Maintenance

Planned Maintenance

Condition-Based Maintenance

Predictive Maintenance

MRO in manufacturing

MRO in aviation

MRO in energy and utilities

MRO procurement, inventory, and software

Inventory

Software

Where time series databases fit in an MRO strategy

Common MRO challenges

Fragmented Spending

Reactive Maintenance Culture

Poor Data Quality

Excess and Obsolete Inventory

How to improve an MRO strategy

Wrapping up

What does MRO stand for?

What is MRO in business?

What is the difference between MRO inventory and production inventory?

What is MRO in manufacturing?

What is MRO in aviation?

What are MRO supplies?

Why is MRO important?

What is the difference between preventive and predictive maintenance?

What is a CMMS and how does it relate to MRO?

Telegraf Enterprise Beta is Now Available: Centralized Control for Telegraf at Scale

What is Telegraf Enterprise?

Meet Telegraf Controller: your Telegraf control plane

Configuration Management That Doesn’t Collapse Under Growth

Fleet-Wide Health Visibility

Designed to fit your telemetry stack

Why we’re running this beta

Enterprise support that matches production expectations

Who is Telegraf Enterprise for?

Packaging: free and enterprise options

Telegraf Controller

Telegraf Enterprise

Getting started with Telegraf Controller

Unifying Telemetry in Battery Energy Storage Systems

The operational reality of modern BESS systems

The fragmented reality of BESS telemetry

Operational cost of data silos

What unified telemetry actually means

How unified telemetry works

Operational impact

ju:niz Energy Deployment

The bottom line

A New Scale Tier for Time Series on Amazon Timestream for InfluxDB

Scaling for the mission-critical