Operations | Monitoring | ITSM | DevOps | Cloud

It has been a little over 2 months since 1.3.0 was released. We started prepping for the 1.4.0 release several weeks ago; however, when I was writing this very blog post for the release, we discovered some confusing stats from the new statistics objects (which we’ll talk about in a bit). After sorting that out, we played the usual game of, “Wait, don’t release yet!

Logging is vital to the success of any IT project. With solid logging practice, you can troubleshoot errors, find patterns, calculate statistics, and communicate information easily. With the size and complexity of modern systems, performing these actions involves various analysis activities. One of these important analysis activities is anomaly detection. What is anomaly detection, and where does it fit in all of this? That’s what this post is about.

AWS S3 buckets are an indisputably powerful—and extremely well-organized—DevOps tool. Standing for “simple storage service,” the S3 is the lowest tier offered for AWS storage, but it is also the most indispensable. S3 buckets store data for immediate recall, the most active components in Amazon’s arsenal of storage options. They can store a variety of developer applications and up to five terabytes of data each.

Today, we are facing an unprecedented situation. The COVID-19 pandemic is affecting everything we know -- our families, our businesses, our communities, and our way of life. In these tough times, many organizations have resorted to mandatory remote working for employees so they can still be productive and safe. Saas productivity tools like Zoom, Slack, G-Suite and Office 365 became seemingly mandatory in this new distributed workplace.

Alcide recently introduced Alcide kAudit, an automatic tool for analyzing Kubernetes Audit logs. This tool focuses on detecting non-compliant and anomalous behavior of users, automated service accounts and suspicious administration operations. Alcide’s recent integration with Sumo Logic enables users to gain full access to insights and real-time alerts from Alcide kAudit.

In part one, we introduced the duality of observability, controllability. As a reminder, observability is the ability to infer the internal state of a "machine” from externally exposed signals. Controllability is the ability to control input to direct the internal state to the desired outcome. So observability is a loop problem. And we need to stop treating it as the end state of our challenge in delivering performant, quality experiences to our users and customers.

The waves of change are certainly upon us and businesses are being forced to adapt at a record pace. Current world events have caused a jarring shift in all aspects of our lives, accelerating major changes in how we live and work. An unprecedented number of people are now working from home. Those of us working in IT Operations are no exception. Many companies are implementing a Distributed IT Operations Center (D-NOC) approach to address this new reality.