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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.

Choosing the proper Home Assistant hardware can be overwhelming. Whether you’re new to home automation or a seasoned pro, the hardware you select can make or break your experience. This comprehensive guide will demystify the requirements, delve into the various options, and help you make an informed decision. From the compact Raspberry Pi to the powerful Intel NUC, we’ve got you covered. So, strap in, and let’s dive into the world of Home Assistant hardware!

Monitoring starts with data, but action depends on timely alerts. When an alerting workflow relies on scheduled queries or external checks, engineers miss short windows where values shift and conditions form. The alerting plugin closes that gap by evaluating alert rules inside InfluxDB 3 as new values arrive, enabling faster detection and more responsive monitoring.

Modern systems generate huge volumes of time series data. Advances in hardware and edge instrumentation enable sensors and applications to capture new values every second—or faster—which makes high-frequency measurement easy and affordable. When applied effectively, this steady flow of data reveals early warning signs, highlights subtle performance shifts, and helps teams understand how systems behave in real-time.

InfluxDB is the go-to database for developers working with high-velocity time series data for use cases like application performance monitoring and real-time analytics. But InfluxDB exhibits its true power when combined with the right tools and integrations. The tools covered in this blog post can help at all stages of your workflow, from data collection to visualization and analysis, so you can get the most out of your InfluxDB deployment.

Your smart home devices generate vast amounts of scattered data—this tutorial shows you how to centralize it into a unified platform using InfluxDB 3 and Grafana. You’ll not only track your home’s vital signs but also learn professional software development concepts, such as time series database design and building resilient data pipelines, applicable to various monitoring and analytics systems. Before we begin, ensure you have.