Operations | Monitoring | ITSM | DevOps | Cloud

Data is more than 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 organizations a live view of operations and the ability to catch issues early, adjust quickly, and operate more efficiently. However, capturing data alone does not create value.

The benefits of continuous delivery are well known these days: rapid feedback, speed of innovation, reduced fault recovery time, and increased confidence in release processes. Along the same lines, those who release less frequently are likely to encounter more stress. Continuous delivery is a spectrum; it doesn’t have to mean blasting every commit to all production environments at once. So, how do we strike a balance between speed and safety?

InfluxDB 3.3 is now available for both Core and Enterprise, which introduces new managed plugins for the Processing Engine, making it easier to address common time series tasks with just a plugin. On top of that, 3.3 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.

When Microsoft Flight Simulator 2024 generates telemetry data at 30-60 FPS, capturing and processing that stream in real-time becomes a fascinating engineering challenge. We built a complete telemetry pipeline that reads over 90 flight parameters through FSUIPC, streams them to InfluxDB 3 Enterprise, and displays them in real-time dashboards that respond in under 5 milliseconds.

Great observability doesn’t just collect metrics—it tells you exactly what’s broken, why it’s broken, and what to do about it. InfluxDB 3 Enterprise delivers this through real-time ingestion, fast queries, and scalable storage. InfluxDB 3 Explorer provides the intuitive interface your team needs for database management, data ingestion, querying, and visualization without the usual complexity.

Businesses across industries rely on time-stamped data to track system health, monitor performance, and improve operations. Whether it’s sensors on a factory floor or usage logs from a SaaS platform, time series data reveals how things change. As businesses digitize operations and add connected devices, sensors produce growing streams of time-based data. This opens the door to faster analytics and smarter automation. But legacy approaches can’t keep up.

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.