deployment artifacts and normally does not touch (or know about) source code. But in most cases, in order to run unit tests, you need access to the source code of the application. The current crop of libraries needed for the tests. This means that you are forced to use your CI solution again in order to run the smoke tests. The end result is a mixture of CI-CD-CI-CD components that goes against underlying issues such as not knowing exactly when your environment has finished with the deployment in order to trigger the tests. The same scenario is trivial to execute with a traditional CI/CD pipeline

structure for manifests: ○ Monorepo; or ○ Separate repos for base/environments ● Managing secrets ● Order dependent deployments ● Non-declarative requirements ● Integrating with CI/CD tools (Jenkins, OpenShift others Argo CD - Order Dependent Deployments https://argoproj.github.io/argo-cd/user-guide/sync-waves/ ● Sometimes you have cases where you need to deploy things in a specific order ○ Subscribe Operator

for automating the deployments. A Kubernetes cluster applies a set of changes in the correct order and in a timely manner. • Convergence: Kubernetes will keep trying to make the update until it An essential component of GitOps is feedback and control. But what is meant exactly by this? In order to have control so that developers can go faster, they need observability built into their deployment

done. In this way, application development teams are internal customers of the platform team. In order to properly fulfill its role as an internal service provider, the platform team must take a strategic Kubernetes, and also sought to leverage the ecosystem of available cloud-native open source projects in order to remain innovative and improve their time to market. However, they needed to implement cloud-native

for using the principles of “GitOps” to take advantage of Kubernetes declarative infrastructure in order to deploy more often, and with more reliability. In this session, you’ll learn the principles of

from Git to clusters ● Drift detection, visualization and correction ● Granular control over sync order for complex rollouts ● Rollback and rollforward to any Git commit ● Manifest templating support

It involves gathering and analyzing various types of data including logs, metrics, and traces in order to gain insights into a system’s performance, health, and other issues. Paired with open source monitoring

if that key is rotated or expired, you need to make sure that every cluster gets the new key in order to work. The number of updates required for each change can lead to a lot of overhead in managing