Operations | Monitoring | ITSM | DevOps | Cloud

Data from monitoring tools like Datadog are useful for developers to help them understand whether the code they've deployed is healthy or needs to be fixed or rolled back, or when there is an incident to investigate. As a deployment mission control, Sleuth helps developers see metrics data from a developer-centric point of view - by deployment - and interpret such data for them. ‍‍

The SolarWinds hack, which has affected high-profile Fortune 500 companies and large U.S. federal government agencies, has put the spotlight on software development security — a critical issue for the DevOps community and for JFrog. At a fundamental level, if the code released via CI/CD pipelines is unsafe, all other DevOps benefits are for naught.

We get it, installing Artifactory and the JFrog DevOps Platform on Kubernetes can be daunting. As easy as we’ve sought to make it with our official JFrog installation Helm charts, there are a lot of decisions to be made. That’s meant to give you the widest possible choice for how to best fit your JFrog installation to your infrastructure. But choice can be overwhelming, too.

A typical modern DevOps pipeline includes eight major stages, and unfortunately, a release bottleneck can appear at any point: These may slow down productivity and limit a company’s ability to progress. This could damage their reputation, especially if a bug fix needs to be immediately deployed into production. This article will cover three key ways using data gathered from your DevOps pipeline can help you find and alleviate bottlenecks in your DevOps pipeline.

Continuous Deployment (CD) requires setting up your infrastructure and automation to update your solution with the latest code change from the main branch. That’s what we call “Liquid Software”. Full automation makes your deployment seamless, less error prone, faster and it makes the feedback loop shorter because you can now deploy after each change. Achieving continuous deployment requires the following elements.

Bitbucket Pipelines now allows steps with a manual trigger to be used in parallel groups, satisfying one of the highest voted feature requests. This feature provides more flexibility in Pipelines, allowing teams to configure pipelines with multiple options and then only run the steps they actually need to run, at the time they want. For example you can choose which environments should be deployed for individual developers, giving them different environments to test and do their work.