Operations | Monitoring | ITSM | DevOps | Cloud

One of the most compelling features of InfluxDB 3 is its built-in Python Processing Engine, a versatile component that adds powerful, real-time processing capabilities to both InfluxDB 3 Core and Enterprise. For those familiar with Kapacitor in InfluxDB 1.x or Flux Tasks in 2.x, the Processing Engine represents a more streamlined, integrated, and scalable approach to acting on data.

Time series data innovators and open source community members following us will know that we recently released two new products: InfluxDB 3 Core and InfluxDB Enterprise. InfluxDB 3 Core is a high-performance recent data engine optimized for real-time monitoring, data collection, and streaming analytics use cases. InfluxDB 3 Enterprise builds on Core’s foundation by integrating historical analysis and data compaction, enabling efficient querying over extended time ranges.

2024 was a huge year for Apache DataFusion, and 2025 is looking to be even bigger. The project officially became a top-level Apache Software Foundation project, achieved best-in-class performance for querying Parquet files in the ClickBench benchmark, and had a research paper accepted for SIGMOD.

In early January, we announced the launch of InfluxDB 3 Core and InfluxDB 3 Enterprise in public alpha. One of the newest included features is the InfluxDB 3 Processing Engine–a Python-based VM built to enable data transformation, enrichment, downsampling, alerting, and more, all from within the database itself. One month later, we’re excited to deliver a big update enabling new ways to interact with and transform your data.

The world runs in real-time. From industrial automation and IoT monitoring to AI-powered analytics, developers rely on time series data to power critical systems and make split-second decisions. But as workloads grow, so do the challenges: keeping queries fast, ensuring high availability, and scaling efficiently without adding operational complexity. Not having to worry about operational overhead enables companies to focus on deriving value from their data.

Amazon Timestream for InfluxDB expands offering with Read Replicas, delivering enterprise-grade scalability and reliability to time series workloads on AWS.

Cybersecurity in the Industrial Internet of Things (IIoT) is often overlooked despite powering critical infrastructure such as energy grids, telecom networks, factories, robotics, and aerospace, all of which are prime targets for cyberattacks and data breaches. A single breach can disrupt essential services or expose sensitive data. So, how do we stay ahead of bad actors and proactively defend these systems?

As part of the InfluxDB 3 Core and InfluxDB 3 Enterprise public alpha, the Last Value Cache (LVC) is available for testing. The LVC lets you cache the most recent values for specific fields in a table, improving the performance of queries that return the most recent value of a field for specific time series or the last N values of a field, typical of many monitoring workloads. With the LVC, these types of queries return in under 10ms.

Forecasting is all about predicting the future—in data science, it is one of the key skills in dealing with time series data, such as stock price prediction, sales forecasting, logistics planning, etc. In this tutorial, we’ll learn how to forecast the notorious weather pattern of London, UK, using the following free and open source technologies.

This blog covers the nitty-gritty of essential command-line tools and workflows to effectively manage and interact with your InfluxDB 3 Core and Enterprise instances. Whether you’re starting or stopping the server with configurations like memory, file, or object store, this guide will walk you through the process. We’ll also look at creating and writing data into databases using authentication tokens, exploring direct line protocol input versus file-based approaches for tasks like testing.