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!
Today we announce InfluxDB 2.0 Open Source’s first official release candidate (RC). This represents a final version of the software as we move towards general availability. We appreciate all the feedback from our users over the last few years and realize that getting to this stage has taken longer than any of us predicted.
Suffering from severe headaches during meetings, feeling fatigued and lethargic due to lengthy powerpoints and monologues (you know, the ones that go on and on)? If that sounds oh so familiar to you, we have good news: it’s not you. And (usually) neither are your colleagues nor their presentations to blame. More often than not, the culprit for a “meeting hangover” is “bad”, stale air.
Machine learning in the Elastic Stack provides you with an intuitive way to detect anomalies in vast data sets. But even the most sophisticated anomaly detection job might not reveal the root cause of anomalous behavior. After an anomaly is detected, you may need to dive into further analysis, review multiple corresponding metrics, and investigate how they relate to the anomalous spike.
The next 10 years will redefine banking. What will differentiate top banks from their competitors? Data and derived insights.
