Continuous delivery (CD) is more than a buzzword. In fact, CD today drives development and operations (DevOps) teams at enterprises around the world.

I'm on the 6:10pm Amtrak Cascades out of Seattle, scheduled to arrive in Portland at 10:00pm. After two intense days in Bellevue, I'm exhausted, but wired. A group of young guys two rows up are loudly discussing their favorite AWS services; I wonder if they're also returning from ElixirConf, but then I remember that this is Seattle—half the city works in tech.

When a critical incident hits, the implications for the business could not be more profound. Whether it’s a productivity system that powers the efficiency of thousands of employees, or an online service that serves millions of customers and drives the company’s revenues - no organization can afford anything less than an immediate and effective resolution.

Kubernetes and OpenShift are powerful and flexible. They’re also complex to setup, monitor and maintain at scale. Here’s a sneak peek into what we monitor in OpenShift, as well as some hard-earned advice on how our strategy might benefit your own environments.

At first glance, applying machine learning to Continuous Delivery might sound a bit like cracking a peanut with a sledgehammer. I mean, how hard can deployment automation actually be? As it turns out, it’s way more complex than we think.

At Sensu, we define monitoring as "the action of observing and checking the behavior and outputs of a system and its components over time." Essentially, you want to be made aware of a problem before your users are.

Last week TimeShift took a vacation, but the Grafana Labs team stayed busy. We announced an important security fix for 5.2.3 and 4.6.4 specifically for LDAP and OAuth authentication. Please read the announcement and upgrade immediately if you haven’t already.