This article describes an end-to-end solution built with open source components InfluxDB and Grafana and the ogamma Visual Logger for OPC, to collect industrial process control data, analyze it in streaming mode, and visualize it in a dashboard.

Machine learning pipelines have evolved tremendously in the past several years. With a wide variety of tools and frameworks out there to simplify building, training, and deployment, the turnaround time on machine learning model development has improved drastically. However, even with all these simplifications, there is still a steep learning curve associated with a lot of these tools. But not with Elastic.

In this post, you’ll learn what causes high series cardinality in a time series database and how to locate and eliminate the culprits. First, for those of you just encountering this concept, let’s define it: The number of unique database, measurement, tag set, and field key combinations in an InfluxDB instance. Because high series cardinality is a primary driver of high memory usage for many database workloads, it is important to understand what causes it and how to resolve it.

Elastic Cloud on Kubernetes (ECK) is the official operator for provisioning Elastic Stack deployments in Kubernetes. It orchestrates not only day-one provisioning, but also has the processes and best practices for day-two management and maintenance baked in. If you want to run your own Elastic Stack deployment on Kubernetes, then look no further than ECK!