Operations | Monitoring | ITSM | DevOps | Cloud

Data systems rarely operate as isolated components. Machines depend on sensors, services rely on other services, and devices exchange data through shared gateways. When something changes, the impact often spreads beyond a single metric. To trace how changes move through complex systems, many teams turn to graph-style analysis to map dependencies and follow cause and effect.

For IT and OT engineers managing Battery Energy Storage Systems (BESS) and other distributed energy resources (DER), the challenge isn’t just dealing with energy. It’s a data problem, or managing the massive stream of real-time telemetry these systems generate. For example, a BESS site produces a constant stream of time-series data from BMS, PCS, SCADA, EMS, and more, and operating it means ingesting, correlating, and acting on that data in real time. And this challenge changes with scope.

This post covers how to get started with Home Assistant and Grafana, including setting up InfluxDB and Grafana with Docker, configuring InfluxDB to receive data from Home Assistant, and creating a Grafana dashboard to visualize your data. It provides a comprehensive guide for real-time monitoring and analysis of Home Assistant data. In this tutorial, you’ll learn how to integrate Grafana with Home Assistant using InfluxDB.

Regression analysis with time series data in Python provides a basis for understanding how values change over time. By following this guide, you’ll understand regression as applied to time series data, how to prepare it in Python, and how to create regression models that’ll help discover trends and influence decisions. With the vast amount of time series data generated, captured, and consumed daily, how can you make sense of it?

If you’re working with sensors, machines, or embedded systems, your primary goal is simple: no unplanned downtime and smooth operations. This means detecting errors and taking action as soon as possible, ideally preventing them through predictive maintenance before they become critical issues.

A time series database has a straightforward definition: it’s a database purpose-built for efficiently ingesting, storing, and querying time series data. Time series data is any data with a timestamp, collected regularly or periodically, that you’ll often visualize on graphs where the X-axis is time. This definition doesn’t quite tell you what sets it apart from other types of databases, though.

InfluxDB 3.8 is now available for both Core and Enterprise, alongside the 1.6 release of the InfluxDB 3 Explorer UI. This release is focused on operational maturity and making InfluxDB easier to deploy, manage, and run reliably in production. InfluxDB 3 Core remains free and open source under MIT and Apache 2 licenses, optimized for recent data. InfluxDB 3 Enterprise builds on that foundation with long-range querying, clustering, security, and full operational tooling.

Over the past two decades, we’ve witnessed the instrumentation of virtually everything in the aerospace industry, from manufacturing floors to satellites orbiting Earth. And it’s no longer just NASA and other government organizations leading the charge. The commercial space industry has grown exponentially, with private companies developing everything from GPS satellites to electric VTOL aircraft.