Operations | Monitoring | ITSM | DevOps | Cloud

Summary This guide walks through an end-to-end Grafana and InfluxDB 3 integration using a realistic dataset you generate yourself. The tutorial covers getting data in, transforming it, connecting Grafana, and building real dashboards. Table of Contents InfluxDB and Grafana are the most common pairing in time series monitoring, and division of labor between them is simple.

Summary Small and midsize businesses are often flying blind when it comes to security breach detection. An affordable way to address this issue without the complexity of SIEM is by modeling security events as time series data. This architecture takes audit logs from SaaS platforms and normalizes activities like logins, downloads, and token creation to establish behavior baselines that can be used to detect anomalies indicating security breaches. Table of Contents.

Summary Telegraf Controller 1.1 lets teams make fleet-wide configuration changes with a single edit using Global Constants, Configuration Groups, and Configuration Aliases. Configuration Versioning makes every change traceable, comparable, and reversible. High availability, available in Telegraf Enterprise, automatically fails over between Controller instances so agents can continue pulling configurations and reporting health if an instance goes down. Table of Contents.

Summary Downsampling turns high-frequency time series data into lower-resolution summaries. In InfluxDB 3, you can calculate those summaries by querying with SQL or materialize them on a schedule with the Python Processing Engine. Table of Contents This tutorial demonstrates both approaches using the InfluxDB 3 Processing Engine’s built-in bird tracking simulator plugin. You will generate telemetry, aggregate it into 10-second windows, and validate the result with SQL.

Summary A new async Rust client for InfluxDB. Built for the edge gateways, embedded systems, and high-throughput ingest pipelines where Rust already runs. Table of Contents Time series data shows up wherever the physical world meets software. A satellite constellation streams altitude, power, and thermal telemetry from every spacecraft on every pass. A factory floor running on Industry 4.0 principles instruments every line, every motor, every batch.

Summary Physical AI—machines and industrial systems that sense conditions, reason, and act in the real world—needs two things from operational data: detailed history for training, and real-time telemetry for inference. Traditional data historians weren’t built for either at the speed Physical AI requires. Four gaps result: limited real-time access, compression that strips model-relevant signal, IT/OT fragmentation, and site-by-site architectures.

Summary InfluxDB 3.11 delivers major performance and data management updates for growing time series workloads, with significantly faster queries on recent data, expanded support for wide and ultra-sparse schemas, and a more predictable resource profile under load. InfluxDB 3 Enterprise also adds backup and restore, bulk Parquet import, row-level deletes, and a built-in Explorer UI.

Summary The InfluxDB satellite telemetry demo is a live mission-control application that monitors a simulated fleet of 12 satellites in real-time. It shows how the InfluxDB 3 Processing Engine can detect anomalies as data is written, enrich time series data with third-party data, and power a grounded AI agent using the InfluxDB 3 MCP server to investigate and explain fleet health.

Summary The five most recent plugins from the InfluxDB team are live: Sagemaker, value counter, Chronos forecasting, simple data replicator, and a stock portfolio tracker. Table of Contents The InfluxDB team has released five new Processing Engine plugins. They range from making it easy to call a hosted ML model to pulling in stock market data in real-time. Every one of them can be activated with a few terminal commands.

Summary InfluxDB plays a critical role in LeoLabs’ infrastructure, enabling a lean team to operate with confidence that potential issues will be detected and surfaced in real-time. By offloading the complexity of managing time series data at scale, engineers are free to focus on higher-impact work (such as optimizing their radar network) rather than maintaining and troubleshooting database systems.