Operations | Monitoring | ITSM | DevOps | Cloud

A common problem in monitoring and alerting systems is not just alerting on what you’re seeing but preventing alert storms from overwhelming operators. When a system generates multiple notifications for the same incident, it leads to alert fatigue and can mask other important issues. For time series data, alert fatigue can result in missed anomalies, delayed responses to critical trends, and difficulty distinguishing real performance degradations from noise.

In Industrial IoT for real-time monitoring, timely alerts are crucial. While Slack and email notifications are common, they can be easily missed or buried in a flood of other notifications. SMS and WhatsApp on the other hand, offer a level of immediacy and directness that’s hard to ignore.

InfluxDB 3 Core and Enterprise introduce a powerful new diskless architecture that lets you store your time series data in cloud object storage while running the database engine locally. This approach offers significant advantages: you get the performance of a local database combined with the durability, scalability, and cost-effectiveness of cloud storage. In this tutorial, I’ll show you how to set up InfluxDB 3 Core or Enterprise with Azure Blob Storage as your object store.

Today we’re excited to announce that InfluxDB 3 Core, our new open source product licensed under MIT/Apache 2, and InfluxDB 3 Enterprise are now in beta. InfluxDB 3 Core is a high-speed, recent-data engine that collects and processes data in real-time, while persisting it to local disk or object storage. InfluxDB 3 Enterprise is a commercial product that builds on Core’s foundation, adding high availability, read replicas, enhanced security, and data compaction for faster queries.

Monitoring is only as good as the alerts that surface critical issues before they spiral out of control. With InfluxDB 3 Core and Enterprise, you can extend alerting capabilities beyond built-in solutions by leveraging custom Python processing plugins. Whether you need real-time notifications when thresholds are exceeded or advanced anomaly detection tailored to your infrastructure, developing custom alerting logic ensures you get the right alerts at the right time.

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.