Operations | Monitoring | ITSM | DevOps | Cloud

Manufacturers are under pressure to increase efficiency, reduce downtime, and future-proof their factories. Rising costs, global competition, and shifting customer expectations mean that even small inefficiencies can lead to lost revenue or market share. This is difficult while using legacy systems that limit visibility and adaptability. These outdated systems often operate in silos, making it hard to access real-time data, respond to unexpected issues, or scale with modern technologies.

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.