Splunk’s recent "What Is Your Data Really Worth?" report1 highlighted the importance of data and analytics to financial services companies. In our global survey of business and IT decision makers2, 89% of respondents from financial services companies felt that the intelligent use of data and analytics is becoming the only source of differentiation in the industry.
I recently participated in a webinar exploring the question "What is Your Data Really Worth?" in the context of financial services. Enterprise Strategy Group (ESG), in partnership with Splunk, performed a global research survey of 1,350 business and IT decision-makers across leading economies and industries. Over the course of the webinar we discussed their findings with my participation focused on IT Operations.
This tutorial will show you how can Coralogix provide analytics and insights for the Fastly logs you ship to Coralogix, both performance, and security.
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?
