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The Evolving World of GitOps and Observability

Deepa Ramachandra
Deepa Ramachandra

Is GitOps changing observability as we know it?

GitOps has been the buzzword in the DevOps space for several years. GitOps, to those who are not familiar with it, is an operational methodology for DevOps that leverages a continuous deployment approach with Git as the single source of ‘truth’ for declarative control over both infrastructure and applications. While Git manages and versions the source code of applications, GitOps streamlines the infrastructure provisioning and application deployment processes.

Before looking at what is interesting about GitOps for the Observability space, let’s look at the features of GitOps, which can help us understand why we think GitOps provides better observability.

Best Practices

Process-oriented application management is the future for businesses of all sizes. With increasing vulnerabilities and cloud-native application adaption, geographically distributed workforces, and the need to spend less time on deployment and more time on development, implementing a declarative approach to application deployment is inevitable. GitOps serves as a single source of truth – a single reference point for all matters related to your application. When in doubt, you will always know where to look.

Related Content: When Two Worlds Collide: AI and Observability Pipelines

Declarative Approach

With GitOps, you can manage and define your infrastructure via code. This means cloud VMs, containerized applications, and Kubernetes deployments have their desired configurations saved in Git. All changes made to the GUI or CLI are made via configuration files – a much safer approach. This makes changes to cloud-native infrastructure follow the standard revision processes, such as pull requests, reviews, and merges. All changes are managed through the configuration declarations while the system’s state is versioned in Git. GitOps eliminates the possibility of human errors because manual changes are not permitted, except in some scenarios where it is done via an operator process. The most significant added value is that GitOps brings much-needed traceability, making rollback and recovery easier. Kubernetes and GitOps are a match made in heaven; Kubernetes works declaratively via a yaml file, which is very conducive to a GitOps approach.

Automated CI/CD Pipelines

The truth of the repository dictates the build, test, and deploy steps. GitOps does not use any additional features. Instead, it capitalizes on the capabilities of Git with a declarative process to apply deployment logic. The version in Git is set as desired, then executes the declared operations in sequence to sync the desired settings to the actual state.

In the GitOps model, the development team can work independently on code changes, committing changes into the declarative repository. With all steps of deployment post-commit automated, the changes are implemented to the actual state without the intervention of cross-functional teams. That said, the development team can only commit changes with the necessary review/test processes, as mandated by whatever code quality standards the team follows. The crucial factor that gives GitOps an edge is that the code reviews applicable in the development environment are also relevant to the operational environment.

Why is GitOps suitable for observability?

GitOps is bound to refine the observability aspect of application development. Here are some apparent enhancements that GitOps brings to the current observability process:

Visibility Over Application States

Answers to your questions about your actual and desired system states are easier to find with GitOps. In a declarative structure such as Kubernetes, observability with GitOps is maximized. You can easily find application-related information such as health, configuration status, and drifts in configurations. More importantly, there’s a change log in place for all changes made to GitOps.

Related Content: Splashing into Data Lakes: The Reservoir of Observability

Better Metrics

It is easier to visualize all your infrastructure and application deployments with advanced metrics drawn from GitOps, such as latency, deployment frequency, deployment errors, and processing errors in deployment. This is made possible by automating the CI pipeline.

GitOps is an evolving methodology. Watch the space as we explore and report on how you can use GitOps and observIQ to improve your log management. Comment with your suggestions and contact our support team for any questions about observIQ.

Deepa Ramachandra
Deepa Ramachandra

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