Splunk is a fantastic platform for ingesting, storing, searching and analysing data from logs, metrics and traces from a massive variety of sources. But does that mean we should ignore all of that data that doesn’t fall into these categories, like image and video data for example? Of course not!

For the 7.10 release of Elastic Workplace Search, we highlighted some of the new analytics logging capabilities and took you through a short walkthrough of some sample analysis scenarios. With the 7.11 release we introduced analytics fields, which open up new possibilities for exploring query and click data with helpful dashboards and visualizations.

We are excited to bring you a number of updates for using Elastic Cloud (Elasticsearch managed service) in the AWS Marketplace.

The idea of uncovering the root cause of a problem has a universal appeal. The systems we are dealing with on a daily basis are complex. When an issue occurs, we naturally opt to resolve the deeper underlying problem rather than settle for treating the symptoms or the downstream effects.

We're excited to announce that autoscaling is now available on Elastic Cloud. In our initial release, autoscaling monitors the storage utilization of your Elasticsearch data nodes and the available memory capacity for your machine learning jobs.

Like many other industries, contact centers are increasingly relying on employees working from home. The WFH trend poses new challenges, but it also surfaces issues that were largely ignored before. This article explains how holistic monitoring with Splunk Contact Center Analytics and uberAgent help drive exceptional customer service.

It is incredibly useful to be able to identify the most unusual data in your Elasticsearch indices. However, it can be incredibly difficult to manually find unusual content if you are collecting large volumes of data. Fortunately, Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data. And with machine learning, the larger the dataset, the better.

I was recently on the Changelog Podcast talking about Elastic’s recent change away from open source licensing. I’m at 1:02:45 to 1:24:03, but the whole thing is pretty interesting if you have time to listen. This is where #InfluxDB is headed. No more open core, we're going to a combination of cloud offering, or if on-premise, a complementary offering to the open source. It'll take us time to get there, but that's the vision. Commercial complements the open source rather than replace.

Back in our 7.10 release of the Elastic Stack, we announced the beta of our Ruby and Python clients for Elastic Enterprise Search. Now, with 7.11, both the Ruby and Python clients are generally available. We’ve also begun work on a PHP client. All client source code for both enterprise-search-ruby and enterprise-search-python is available on GitHub. Documentation on how to get started with each client is available on elastic.co.