In this article, we are going to have a quick introduction to Logstash, a very popular application for collecting, processing and filtering log data – and see how it works. We will review plugins, installation and configuration of Logstash, we will briefly mention Beats, and also compare Logstash to other log collectors and review Logstash alternatives.

Use of AI can be critical when developing systems to support social good, with some inspiring examples using Splunk in healthcare and higher education organisations. According to our State of Dark Data report, however, only 15% of organisations admit they are utilising AI solutions today due to lack of skills. So how can we help organisations unlock the potential of AI?

If you’re new to the Dashboards Beta app on Splunkbase and you’re trying to get started with building beautiful dashboards, the "Dashboards Beta" blog series is a great place to start. This Dashboards Beta app brings a new dashboard framework, intended to combine the best of Simple XML and Glass Tables, and provide a friendlier experience for creating and editing dashboards.

Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.

In this tutorial, we’ll use the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm from scikit-learn with the ADTK (Anomaly Detection Tool Kit) package to detect anomalous CPU behavior. We’ll use the InfluxDB 2.0 Python Client to query our data in InfluxDB 2.0 and return it as a Pandas DataFrame. This tutorial assumes that you have InfluxDB and Telegraf installed and configured on your local machine to gather CPU stats.