Operations | Monitoring | ITSM | DevOps | Cloud

Time series data underpins all real-time systems. From high-resolution telemetry to long-range trends, it’s essential for monitoring, automation, predictive maintenance, and operational insight. But it’s also hard to work with: high cardinality, shifting schemas, and time-based queries make even basic tasks feel heavy.

Time series data powers nearly every modern system, from industrial equipment and energy grids to financial platforms and digital services. Devices and software continuously generate streams of time-stamped metrics that reflect how systems perform moment to moment. Most businesses collect this data, but far fewer utilize its full potential. Storing information and reviewing dashboards offers limited value.

In this tutorial, let’s walk through setting up a modern TIG stack in 10 minutes. TIG stands for three popular open source tools that complement each other: Telegraf, InfluxDB 3, and Grafana. They are often used to collect, store, and visualize time series data from servers, containers, APIs, or even IoT devices. We will be using a read-to-use GitHub repository that includes.

InfluxDB 3.2 is now available for both Core and Enterprise, bringing the general availability of InfluxDB 3 Explorer, a new UI that simplifies how you query, explore, and visualize data. On top of that, 3.2 includes a wide range of performance improvements, feature updates, and bug fixes. InfluxDB 3 Core is free and open source, optimized for recent data, and licensed under MIT and Apache 2.

The following serves as a practical guide for those looking to simplify their architecture by migrating to a Rust monolith. Earlier this year, the platform team at InfluxData undertook a major rewrite of our core account and resource management APIs, moving from Go to Rust and from a microservices architecture to a single monolith. This change supported a new administrative UI for InfluxDB Cloud Dedicated and aligns with our broader effort to rewrite the InfluxDB database engine in Rust.

Modern data centers generate enormous volumes of telemetry from servers, switches, cooling systems, power infrastructure, and environmental sensors. Operations engineers must capture, store, and analyze this data in real-time to monitor uptime, maintain energy efficiency, and perform predictive maintenance using AI. Legacy monitoring systems struggle to meet today’s volume, cardinality, and latency demands.

Data plays a critical role in shaping operational decisions. From sensor streams in factories to API response times in cloud environments, organizations rely on time-stamped metrics to understand what’s happening and determine what to do next. But when that data is inaccurate or incomplete, systems make the wrong call. Teams waste time chasing false alerts, miss critical anomalies, and make high-stakes decisions based on flawed assumptions.

Time series data, such as IoT sensor readings or stock market ticks, flow in fast, often at a rate of millions of points per second. Querying this data, especially years of historical records, can be slow and painful if using a nonspecialized database rather than a time series database like InfluxDB.

Data isn’t just a record of what happened—it shapes what happens next. Across industries, connected devices continuously stream time-stamped data that reflects the current state of machines, environments, and systems. This steady flow gives businesses a live view of their operations and the opportunity to catch issues early, adjust quickly, and operate more efficiently.