An organization at Level 2 in the Observability Maturity Model has built on the foundation of their monitoring capabilities and taken the first steps into observability. In recent years, two major trends have driven the need for the deeper insights that observability can provide.
What is problem management? In ITSM terms, an incident is a single unplanned interruption or reduction of the quality of an IT service. A problem, on the other hand, is a larger issue. ITIL™ defines a problem as “a cause or potential cause of one or more incidents.” Problem management entails the set of processes and activities responsible for managing the lifecycle of all problems that could happen in an IT service.
Whether you are a Site Reliability or Network Engineer, or simply involved in monitoring a digital service, you know by now that if DNS is not working properly – your users are experiencing an outage. However, despite its importance in ensuring the resilience and availability of the web, DNS is often not monitored correctly, which can mean undetected outages and any associated ripple effects on your business.
“Give customers the power to help themselves. Self-service options allow for faster problem resolution while reducing strain on your support teams.” – our friends at ServiceNow Self-service is a crucial component to any DevOps strategy. Many IT organizations still depend on manual and ticket-driven workflows with strong reliance on dedicated teams to make simple and frequent change requests. Unfortunately, these traditional models don’t scale.
In Cribl Stream and Cribl Edge, you can operate on your observability event data in flight, all the way down to the field level. Instead of writing complex regex to wrangle JSON and other structured formats, use Cribl’s built-in functions and extensibility to get the results you want. You’ll see formerly complex situations become easier to address and manage over the long term. In this blog, we’ll cover two troublesome use cases.
In 2011, a Netflix engineering team introduced the concept of chaos engineering with its release of Chaos Monkey. This was initially an in-house tool developed to orchestrate fault injection that Netflix eventually made open source. However, the reliance of Chaos Monkey on Spinnaker, another Netflix engineering innovation, establishes some limitations.
There is a key difference between having more data and having more answers. That was the theme for Kentik at Networking Field Day 29.
This post, authored by CircleCI Senior Technical Content Marketing Manager Jacob Schmitt, was originally published on The New Stack. One of the privileges of working in the continuous integration space is the unique perspective it offers into how software teams organize their work to deliver value quickly without sacrificing quality, security, or developer happiness. At CircleCI, we support more than 2 million developers running 90 million build, test, and deploy jobs each month.
