Operations | Monitoring | ITSM | DevOps | Cloud

Telegraf is incredibly good at what it does: collecting metrics, logs, and events from just about anywhere and sending them wherever you need. But once Telegraf becomes part of your production telemetry pipeline, spread across environments, teams, regions, and edge locations, the hard part isn’t installing agents; it’s operating them. Configs drift. “Temporary” overrides linger. Rolling out changes across hundreds (or thousands) of agents becomes a careful, manual process.

Battery energy storage systems (BESS) play a critical role in modern energy infrastructure. Utilities rely on these systems to balance renewable generation, stabilize grid operations, and respond to changing electricity demand. As deployments scale in size and complexity, operators require continuous insight into battery health, system performance, and grid interaction. Operators rely on telemetry generated across several operational platforms.

When we first announced the availability of InfluxDB 3 Core and Enterprise on Amazon Timestream for InfluxDB last year, we set a new standard for managed time series on AWS. We gave developers a simple way to harness high performance at scale while removing the burden of infrastructure management. But as our customers have taught us, “at scale” is a moving target. Across Industrial IoT, physical AI, and real-time observability, data is growing in both volume and resolution.

Industry 4.0 is the term used to describe the fourth industrial revolution, a name given to the integration of physical and digital systems, which includes the internet of things (IoT) and artificial intelligence that are transforming a huge number of industries. At a high level, its goal is to create an efficient, automated process for creating products or services that can be adapted quickly and efficiently to changing customer needs.

No one wants to stare at a plant and guess if it needs water. It’s much easier if the plant can say, “I’m thirsty.” A few years ago, we built Plant Buddy using InfluxDB Cloud 2.0. The linked article is still a great guide for cloud-first IoT prototyping as it shows how quickly you can connect devices, store time series data, and build dashboards in the cloud with the previous version of InfluxDB. But this time, the goal was different.

Battery Energy Storage Systems (BESS) operate as revenue-generating grid assets that capture surplus electricity, deploy power during demand spikes, and support frequency control. By shifting energy across time, they stabilize grid conditions, enable renewable integration, and execute market dispatch commitments. When systems respond as designed, stored capacity becomes a flexible, monetizable supply. But BESS performance depends on precision and availability.

Critical infrastructure is under siege. The systems that control our power grids, water treatment plants, and oil pipelines weren’t designed for a connected world. This post covers what security measures teams need to understand and how time series monitoring can help turn SCADA’s weaknesses into a security advantage.

Battery Energy Storage Systems (BESS) represent one of the most unforgiving environments for real-time data. Unlike a passive asset, a battery is a complex electrochemical system where safety and revenue are determined by split-second decisions. In this context, “average” latency can become a serious problem. Performance depends entirely on one key question.

In simple terms, predictive analytics is a form of analytics that tries to predict future events, trends, or behaviors based on historical and present data. You can achieve this goal in different ways, each involving trade-offs between accuracy and cost.

We recently sat down with founder and principal consultant at recultiv8, Coenraad Pretorius, who drew on his years of data engineering experience in the manufacturing and energy sectors to share key industrial IoT insights.