Operations | Monitoring | ITSM | DevOps | Cloud

Curious how time series forecasting fits into your InfluxDB 3 workflows? Let’s build a complete forecasting pipeline together using InfluxDB 3 Core’s Python Processing Engine and Facebook’s Prophet library. InfluxDB 3 Core’s Python Processing Engine dramatically lowers the barrier to entry—not just for experienced developers but for anyone with a basic understanding of time series data and Python.

After months of development, testing, and community feedback, we’re excited to announce the general availability (GA) release of InfluxDB 3 Core and InfluxDB 3 Enterprise. This release brings us closer to our vision for InfluxDB: a time series database that helps developers solve the problem of collecting, analyzing, monitoring, and acting on data across sensors, networks, servers, and applications. We view time series as a way to analyze, monitor, and act on data through time.

InfluxDB 3 Core is an open source, high-speed, recent-data engine; InfluxDB 3 Enterprise adds performance, high availability, security, and scalability for mission-critical workloads Built-in Python Processing Engine brings collection, transformation, monitoring, alerting, and automation on time series data.

Sometimes silence isn’t golden; it’s a red flag. Whether you’re monitoring IoT sensors, system logs, or application metrics, missing data can be just as critical as abnormal data. Without visibility into these gaps, you risk overlooking potential failures, security threats, or operational inefficiencies. In time series workflows, detecting silence is often the first sign of trouble—whether it’s a network issue, device failure, sensor failure, or stalled process.

Part 2: Optimizers in Apache DataFusion In the first part of this post, we discussed what a Query Optimizer is and what role it plays and described how industrial optimizers are organized. In this second post, we describe various optimizations found in Apache DataFusion and other industrial systems in more detail.

As your time series data grows, managing increasing workloads can quickly become a headache. High data ingestion rates, numerous (and complex) queries, intensive processing tasks, and routine maintenance like data compaction often compete for limited resources. This leads to unpredictable performance and slower response times, and common solutions often introduce operational complexity.

Sometimes Query Optimizers are seen as a sort of black magic, “the most challenging problem in computer science,” according to Father Pavlo, or some behind-the-scenes player. We believe this perception is because: However, Query Optimizers are no more complicated in theory or practice than other parts of a database system, as we will argue in a series of posts: Part 1: Part 2: After reading these blogs, we hope people will use DataFusion to.

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.