Operations | Monitoring | ITSM | DevOps | Cloud

I recently chatted with Adam DeMattia from leading research and analyst firm ESG in a webinar about data use maturity in financial services. According to the research1, 21% of financial services firms identify as data innovators (compared to 11% of global respondents) — those who make smarter use of data as a matter of strategic importance.

This tutorial will show you how can Coralogix provide analytics and insights for the Fastly logs you ship to Coralogix, both performance, and security.

Since various versions of lockdown around the world kicked in, we’ve all been forced to learn how to live as if we’ve just awoke from a deep stasis into a new reality in which everything on earth works differently. It was nearly that abrupt. And if there was ever evidence of how business adaptability is related to business success, it is now.

Digital transformation is reshaping every aspect of our lives—from health to education to economic prosperity, and data is at the heart of it. At Splunk, we are bringing data to everything, enabling organizations worldwide to investigate, monitor, analyze and act on their data across IT, Security, and DevOps use cases. Through this digitization, we see customers accelerate their journey to the cloud for increased agility, reduced costs, and faster time-to-market.

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!

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?