Welcome to the second installment of the Dashboards Beta blog series! I’m your host, Aditya Tammana, and today we have a very special guest – v0.5 of the Dashboards Beta app on Splunkbase!
This post was written by InfluxAce Nikki Attea A long time ago in a galaxy far, far away the phrase “May the Force be with you” originated to wish those good luck before parting ways, oftentimes before a journey or battle.
Apache Solr was always ready to be extended. What was only needed is a binary with the code and the modification of the Solr configuration file, the solrconfig.xml and we were ready. It was even simpler with the Solr APIs that allowed us to create various configuration elements – for example, request handlers. What’s more, the default Solr distribution came with a few plugins already – for example, the Data Import Handler or Learning to Rank.
As Elasticsearch users are pushing the limits of how much data they can store on an Elasticsearch node, they sometimes run out of heap memory before running out of disk space. This is a frustrating problem for these users, as fitting as much data per node as possible is often important to reduce costs. But why does Elasticsearch need heap memory to store data? Why doesn't it only need disk space?
There’s no question that subscription-based businesses are an incredibly popular revenue model in today’s economy. While single transaction revenue models tend to fluctuate due to the seasonality of markets, subscription plans offer much more consistent and predictable revenues. Although the subscription revenue model can certainly be advantageous over one-off transactions, these businesses are also notoriously challenging to keep subscribers active on their plan.
As we connect with customers we increasingly hear the need for teams to be more predictive with their data. A big challenge is uncertainty around how to get started, especially when much of their data is unstructured. At Splunk, our goal is to make data — and machine learning — accessible for a broad range of users. The good news is, with machine learning doing even more work on your behalf, you don’t need to be a data scientist to use these advanced capabilities.
Amazon Web Services (AWS) products are countless, and at LogicMonitor, we are working tirelessly to bring monitoring support to as many of them as possible. With so many products and tools already on your plate, we want to make sure that monitoring is not a hassle, but rather a trusted companion. Here, we will focus on the analytics section of AWS and provide some tips on how to utilize the data collected from AWS Athena and Glue.
