Operations | Monitoring | ITSM | DevOps | Cloud

Industrial teams keep telling us the same thing: the data is there, but the stack to act on it isn’t. PLCs, CNCs, SCADA systems, vibration sensors, and quality stations all generate high-frequency telemetry that gets stranded in proprietary historians or stitched together with point integrations nobody wants to own. By the time anyone looks at it, the moment to act has passed.

Networks generate the most data of any system in your stack and have the least patience for stale dashboards. Interface counters tick every second. BGP sessions flap. Flow records arrive in bursts. When something goes wrong, you don’t have 10 seconds to wait for an aggregation to finish.

One of the things we love most about building an open source platform is seeing what the community creates with it, and independent developer Anton Havekes recently built something we just had to share. Anton put together Influx Dashboard, a native iOS app that connects to your InfluxDB instance and brings your time series data straight to your phone. We’re genuinely thrilled to see this kind of work come out of the community.

Every second of a flight test produces a torrent of telemetry from engines, sensors, and control systems. Aerospace teams have captured this data for decades to verify performance and maintain safety, yet analysis often happens long after the mission ends. Engineers wait for downloads, conversions, and compliance checks before they can interpret results. That delay turns telemetry into a historical record instead of a feedback loop.

A battery is a complex electrochemical system where safety and revenue are decided in milliseconds. Cell temperatures, voltages, and state of charge change in real-time; dispatch decisions and thermal alarms must fire in real-time. Anything in between—your data pipeline, your historian, your alerting layer—has to disappear into the background.

InfluxDB 3 Explorer 1.8 is all about writing data and keeping it under control. You can now subscribe to MQTT, Kafka, and AMQP streams directly from Explorer, generate custom sample datasets, stream live sample data continuously into your database, and validate your line protocol and preview the resulting schema before you write it. You can now also view and edit retention periods on both databases and individual tables.

If you’re already running or are familiar with Home Assistant, you’ve likely worked with integrations, maybe a few automations, and possibly MQTT as a way to wire devices together. But webhooks add another layer of flexibility that lets you level up your smart home into a fully-customized, intelligent network. Instead of relying on built-in integrations and being confined to the same local network, you can let external devices and services push events directly into Home Assistant.

Time series autoregression is a powerful statistical technique that uses past values of a variable to predict its future values. This approach is particularly valuable for forecasting applications where historical patterns can inform future trends. In this hands-on tutorial, you’ll learn how to implement autoregressive (AR) models using Python and see how InfluxDB can enhance your time series analysis workflow.

Today at Hannover Messe, InfluxData is announcing a strategic partnership with Litmus to address one of the most persistent challenges in industrial data: getting reliable, contextualized telemetry from the shop floor into production systems. Litmus bridges the gap between OT systems and modern IT infrastructure, while InfluxDB serves as the industrial data hub, giving organizations both real-time operational visibility and enterprise-scale historical analysis in a unified architecture.

In this blog, we’re going to take a look at how you can set up a fully-functioning, robust data pipeline to centralize your data into an InfluxDB instance by collecting and sending messages with the MQTT protocol. We’ll start with a brief overview of the technologies and protocols used in the pipeline, then dive into how you can connect, configure, and test them to ensure your data pipeline is fully functional. It’s going to be a long post, so let’s jump right in.