Operations | Monitoring | ITSM | DevOps | Cloud

Cloud spend optimization is a challenging endeavor for most companies. The major reason for this is that most don’t have sufficient visibility into their live cloud costs, so they can’t tie day-to-day decision-making to those staggering AWS bills that arrive at the end of each month (way too late to make meaningful changes).

A common pattern in serverless architecture is to have a queue before a function. This is great because you can create a second queue for all of the messages that failed in the function execution (or, if we want to put it in terms that don’t sound like we’re aggressively shaming them, we can classify them as having “encountered an error at some point”). This second queue is known as a “dead letter queue” or DLQ for short.

From server setup and hardware configuration to application deployment, traditional end-to-end IT infrastructure management has high overhead and takes a lot of time and effort. With the advent of cloud computing, IT infrastructure has evolved to address the availability, scalability, and agility problems organizations face. Yet the issue of inconsistent configurations remains, because the manual setup of cloud infrastructure can still lead to discrepancies.

With the emergence of Amazon Web Services (AWS) in 2002, the Infrastructure as a Service (IaaS) model kickstarted the public cloud revolution that has permanently changed how IT services are deployed and delivered. According to Gartner’s July 2019 Magic Quadrant for Cloud Infrastructure as a Service report, the annual cloud IaaS market is currently worth $41.4 billion and is expected to grow to $81.5 billion by 2022.

Rancher Rodeos are great events for learning Kubernetes and Rancher beginner-level concepts, with attendees gaining just the right amount of knowledge to get started with provisioning Kubernetes clusters and launching applications. I present frequently at Rodeos, and the scene typically unfolds like this: together with attendees, we are walking through Rancher features, and usually as we’re experimenting with Monitoring, Alerting, and Logging, I see the wheels start turning in their heads.

Going on call and being awakened at a moment’s notice to put out fires when reputation and revenue are on the line is incredibly stressful. And with DevOps teams under increasing pressure to simultaneously release new products faster while ensuring reliability and quality, burnout is a rapidly growing problem. It’s why #HugOps and empathy are becoming so central to the culture of DevOps.