When Splunk was first released almost 20 years ago, it helped many organizations realize the power of logs to gain business insights with pricing based on the volume of data ingested per day. Over the last two decades, the volume, variety, and velocity of data generated by systems and users have grown exponentially. The demands of business and operations have quickly moved beyond compliance and basic reporting.
ClickHouse is an open source database management system, and was originally developed as a backend for Yandex’s Metrica analytics platform. ClickHouse is column oriented, meaning that it can quickly scan through ranges of values in a single column without touching irrelevant values in other columns. This makes ClickHouse well suited for online analytical processing (OLAP).
Jupyter Notebooks are wonderful because they provide a way to share code, explanations, and visualizations in the same place. Notebooks add narrative to computation. The cells compartmentalize steps and reduce the fear or hesitation associated with editing code. In this way, notebooks act as an invitation for experimentation. Today, I want to extend that invitation and apply it to InfluxDB. In this post, we’ll learn how to query our system stats data from InfluxDB v2.0 using Flux.
Remember when you wanted great alerts, so you read our past two blogs about cyclical statistical forecasts and anomalies? Hopefully, the techniques in those blogs gave you some great results. Here we’re going to show you another way of finding anomalies in your data using a slightly different technique.
Over the past 24 months or so, I have been studying investing/trading while also working to become more proficient with Splunk. I like to combine activities and gain momentum, so I decided stock market and economic data would be the perfect way to dig deeper into Splunk and hopefully improve my investing/trading. In the beginning, I only looked at it as a way to learn more about Splunk while using data that was interesting to me.
In a previous blog post, we introduced a new method of monitoring the Elastic Stack with Metricbeat. Using Metricbeat to externally collect monitoring information about Elastic Stack products improves the reliability of monitoring those products. It also provides flexibility with how the monitoring data may be routed to the Elasticsearch monitoring cluster.
What makes data structured or unstructured and how does that affect your logging efforts and information gain? Below we've provided a comparison of structured, semi-structured, and unstructured data. Also below, we discuss ways to turn unstructured data into structured data.
