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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.

Organizations are starting to question whether the value they get from traditional Network Monitoring Systems (NMS) justifies the budget they’ve locked into them.

Time series data is everywhere in modern analytics, from stock prices and sensor readings to web traffic and financial transactions. When working with temporal data in Python, pandas provides powerful tools for handling time-based indexing through its DatetimeIndex functionality. This tutorial will guide you through creating, manipulating, and extracting insights from pandas time indexes with practical examples.