Operations | Monitoring | ITSM | DevOps | Cloud

Organizations are starting to question whether the value they get from traditional Network Monitoring Systems (NMS) justifies the budget they’ve locked into them.

Time series data is everywhere in modern analytics, from stock prices and sensor readings to web traffic and financial transactions. When working with temporal data in Python, pandas provides powerful tools for handling time-based indexing through its DatetimeIndex functionality. This tutorial will guide you through creating, manipulating, and extracting insights from pandas time indexes with practical examples.

Exponential smoothing is a time series forecasting method that uses an exponentially weighted average of past observations to predict future values. In other words, it assigns greater weight to recent observations than to older ones, allowing the forecast to adapt to changing data trends. In this post, we’ll look at the basics of exponential smoothing, including how it works, its types, and how to implement it in Python.

InfluxDB 3 Model Context Protocol (MCP) server lets you manage and query InfluxDB 3 (Core, Enterprise, Dedicated, Serverless, Clustered) using natural language through popular LLM tools like Claude Desktop, ChatGPT Desktop, and other MCP-compatible agents. The setup is straightforward. In this article, we will focus on setting up InfluxDB 3 Enterprise using Docker with Claude Desktop.

InfluxData prides itself on prioritizing developer happiness. A key ingredient to that formula is providing client libraries that let users interact with the database in their chosen language and library. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.

Efficient monitoring delivers fast results: alerts fire within seconds, dashboards refresh continuously, and teams know the moment something changes. Understanding arrives later. An alert may show that a value shifted, but it does not explain why it shifted, how far the impact will spread, or which components truly matter. Teams see the signal, not the system behavior behind it. This gap defines the limit of traditional monitoring. Detection has improved, but explanation has not kept pace.

I’d like to share a new influxctl ease-of-use feature in v2.12.0 that makes it easier to optimize important queries or debug slow ones. influxctl has had the capability to send queries and display the results in JSON or tabular formats for some time.

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.