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.
Managing projects isn’t an easy task, particularly if you’re managing parallel projects with inter-team dependencies. Lack of visibility coupled with difficulty in obtaining the right metrics on time can make it nearly impossible for project teams to track issues, action items, and risks, often resulting in projects running behind schedule, overshooting budgets, or worse, getting stalled due to unforeseen problems.
I’ve always had a good experience using DigitalOcean, a cloud infrastructure provider which offers developers cloud services that help deploy and scale applications that run simultaneously on multiple computers. I’ve used DigitalOcean a lot for my personal projects — for example, to host my personal blog, its stats, and a NextCloud instance, all running in Kubernetes.
Currently, there is no official InfluxDB C language client library. Fortunately, I wanted to do exactly that for capturing Operating System performance statistics for AIX and Linux. This data capturing tool is called “njmon” and is open source on Sourceforge. So having worked out how and developing a small library of 12 functions for my use to make saving data simple, I thought I would share it. I hope it will prove useful for others.
It’s 2021 and somehow, I’m still trying to convince people that content is one of the biggest revenue drivers there is. Back in 2017, there was an influx of “content is king” and “why content is important” blogs on LinkedIn and Medium, but it seems little notice has been taken in the years that have followed.
