InfluxData

🪐 Space operations don’t have an off switch. The infrastructure behind them can’t either. LeoLabs, an aerospace company that provides orbital intelligence services, monitors more than 25,000 objects in Low Earth Orbit (LEO) using a global network of 11 operational radar sites. Behind that mission are 2,000–3,000 connected devices and 1,000+ active alerts that must be monitored around the clock. As its radar network expanded, LeoLabs needed a telemetry platform that could retain years of high-cardinality data while still delivering the speed necessary for engineers to investigate issues in real time. With InfluxDB 3, LeoLabs transformed how it manages telemetry across its global operations, giving a lean engineering team a unified view across every radar site, long-term visibility into system performance, and the confidence to troubleshoot and optimize infrastructure from anywhere. Swipe through the carousel to see how LeoLabs built a telemetry platform that scales with its mission. 🔗 Link to the full story in the comments.

Unlike consumer AI, industrial AI can't rely on plausible outputs. It must be trustworthy, explainable, and grounded in semantically enriched data that ensures every signal is interpreted consistently across systems, enabling reliable decisions at scale. Click through to read the full article from IIoT-World.

Most machine learning pipelines were built for a world where data arrives, gets stored, and is analyzed later. But operational data doesn't work that way. Infrastructure metrics, industrial telemetry, sensor streams, and energy data are most valuable when systems can learn from them as they arrive, not hours after a batch job completes. The new River-based plugins for InfluxDB 3 bring online machine learning directly to streaming data, continuously detecting anomalies, generating forecasts, and building profiles as new data arrives. No separate ML infrastructure. No moving data between systems. Click through the carousel to see how each plugin works and where it fits into your real-time data pipeline.

Sunday read: Modern applications demand faster, more scalable ways to handle time series data, and that’s exactly what Amazon Timestream for InfluxDB 3 delivers. Built for high-performance analytics, it combines next-gen database architecture with fully managed AWS infrastructure to help you ingest, analyze, and act on massive data streams in real time. Think faster queries, efficient storage, and a streamlined path from raw data to insight. Read InfluxData CEO Evan Kaplan’s take at the link, then dive into the full technical breakdown.

Ever wonder what makes time series databases unique? Walk through the carousel to see where traditional architectures start to fail, and what modern systems require→

On the latest episode of the PlusOne podcast, Andrew Lamb, PMC Chair of Apache DataFusion and Staff Engineer at InfluxData, joins Rich Bowen of the The Apache Software Foundation to discuss the evolution of DataFusion, from its roots in Apache Arrow to a top-level Apache project with nearly 1,000 contributors. They also explore what sits under the hood of the modern data stack, how open source communities form around infrastructure end users never see, and what AI coding agents could mean for an embeddable database engine. Click through to watch the full interview!

Offshore drilling generates an enormous volume of real-time data. The challenge isn't collecting it. It's turning it into operational decisions before the moment passes. Seadrill modernized its PLATO analytics platform with InfluxDB Enterprise to process tens of thousands of time series per second across its global fleet, helping teams move from reactive monitoring to real-time operational intelligence. The results speak for themselves. 👉 Click through the carousel to see how Seadrill transformed offshore rig telemetry into millions in savings and measurable efficiency gains.

Battery Energy Storage Systems (BESS) generate a constant stream of telemetry, from cell voltages and temperatures to charge/discharge cycles and inverter performance. The challenge isn't collecting the data. It's making sense of it quickly enough to prevent downtime, optimize performance, and extend asset life. On July 21, Join InfluxData Developer Advocate Cole Bowden for a technical session exploring how time series monitoring helps battery storage operators move beyond reactive alerting and toward predictive operations. You'll learn how to: 🔋 Monitor battery health and performance in real time 📈 Analyze high-frequency telemetry at scale ⚡ Detect anomalies before they impact availability 🏗️ Build monitoring architectures designed for growing fleets and increasing data volumes This session offers a practical look at the role time series data plays in maintaining uptime and operational efficiency. Register here: https://bit.ly/3SF7Sej

With the InfluxDB 3 Processing Engine, you can write your own plugins to solve problems specific to your environment. But you don’t have to start from scratch. We’ve built a library of ready-to-use plugins for common time series tasks, and it’s continuously growing. Use a plug-in as-is, or treat it as a starting point: fork it, customize the logic, and adapt it to your workflow. Either way, the plug-in library is designed to help you move faster. → Check out the carousel for a quick look at what’s new and how it works