May 2025 Summaries

InfluxDB 3.1 is now available for both Core and Enterprise editions, bringing significant improvements in security, performance, and visibility to manage high-volume time series data. The new release includes expanded token support with four distinct types of tokens, automatic expiration, regeneration, and resource-based permissions for enhanced security and administrative flexibility. Additionally, cache reloading has been upgraded for Enterprise, allowing for instant dashboard dropdowns, real-time values, and improved continuity across restarts and scaling events. The database now provides more visibility into its performance with expanded metrics, enabling users to track growth, identify slow queries, and monitor ingestion in real-time. With over 50 enhancements, InfluxDB 3.1 offers improved memory management, faster HTTP queries, and smoother operations. A new update, InfluxDB 3.2, is planned for June with a focus on data management, usability, licensing, and security improvements. Users can upgrade to the latest version and explore the documentation for full details.

InfluxDB 3 bridges the gap between historians and time series databases, making operational data accessible, scalable, and actionable. Historians collect process data but often fall short when businesses need broader insights, deeper analysis, and strategic oversight. Pairing InfluxDB 3 with a historian enables companies to unlock enterprise-wide analytics, enable AI and automation, scale efficiently, make faster, data-informed decisions, and eliminate duplicated efforts. By connecting disparate systems into a unified platform, InfluxDB 3 consolidates data streams, avoids duplicate efforts, and supports predictive maintenance, optimized resource allocation, and smarter decision-making across operations, ultimately driving business growth.

In this demo project, a full-stack machine learning (ML) pipeline is built to forecast time series data and detect model drift in real-time. The pipeline uses PyTorch-based LSTM models for forecasting, InfluxDB 3 for storing time series data and model metrics, and Hugging Face Hub for cloud-based model storage and versioning. The demo showcases how to monitor and adapt ML models as data evolves over time, enabling real-time forecasting and reducing the risk of poor predictions due to model drift. By leveraging the InfluxDB 3 Python Processing Engine, the project can be easily scaled up or down depending on the needs of the system, making it an ideal solution for industries such as industrial IoT, finance, and energy usage monitoring. The demo also highlights the importance of continuous monitoring and adaptation in ML models to ensure reliable predictions.

The text discusses InfluxDB 3, a time series database that enables real-time analytics and high-volume data processing. It highlights the Python Processing Engine, which allows developers to run custom scripts known as plugins to process data, trigger alerts, or integrate with external systems. The document showcases five pre-made plugins contributed by the community, including ones for forecasting time series data using Prophet, sending SMS/WhatsApp alerts, replicating data to remote InfluxDB buckets, and generating statistical metrics for tables. These plugins demonstrate the capabilities of InfluxDB 3's Python Processing Engine and provide a starting point for developers to build their own custom plugins. The document also encourages community contributions and welcomes feedback from users.

The InfluxDB Cloud Dedicated environment has been enhanced with a new administrative user interface (admin UI) designed for speed and usability. This update streamlines everyday tasks, allowing users to focus on working with their data instead of navigating settings. The new UI features simpler navigation, no-code controls, and better visibility, making it easier for developers and teams to manage time series infrastructure at scale. Key improvements include an enhanced account management dashboard, a token portal for managing admin access, and streamlined database management, including no-code controls for creating, viewing, and deleting databases and tables. The new UI is now live and can be accessed directly through the console, with no installation or configuration required.

The InfluxData team is hosting a month-long hackathon starting on May 5, where developers can win up to $8,000 in prizes and swag by building projects using InfluxDB 3 Core and Enterprise. The hackathon aims to encourage the use of new features such as the Processing Engine, which provides an embedded Python VM for collecting, processing, enriching, and taking action on data from within InfluxDB. Additionally, the team is highlighting various server modes in the Enterprise edition, including Ingest, Query, Compact, Process, and All, which provide different levels of performance optimization and flexibility. The hackathon also features a new cache system for optimizing query performance, including the Latest Value Cache (LVC) and Distinct Value Cache (DVC). Participants can find inspiration for projects by looking at previous hackathons, where InfluxData built plugins such as Iceberg table export, natural language to SQL query, data generator, edge to cloud data replication, and time series forecasting and visualization plugin. The hackathon has specific rules, including submission deadlines, eligibility criteria, and judging criteria, which can be found on the registration page at influxdata.com/influxdb-3-hackathon-2025.