Welcome to the second installment of our multi-cloud Kubernetes series. In this week's exploration, we dive into the CI/CD and monitoring challenges awaiting you. We will focus on the theme of externalizing your solutions. If you've been reliant on native tools from clouds like AWS, GCP, or Azure, brace yourself for an introduction into necessary tooling to make your multi-cloud journey possible. We'll explore how third-party tools and strategic adaptations within our ecosystem can elevate the deployment, pipeline, monitoring, and alerting game.

CI/CD: Navigating the Deployment Maze

One of the pivotal challenges is conquering Continuous Integration and Continuous Deployment (CI/CD). Getting your deployments wrong in a multi-cloud environment comes with a hefty price tag — operational overhead, confusion in resource management, and potential disruptions across your diverse cloud and cluster landscape. The first step is clear: steer clear of native cloud deployment tools such as AWS CodeBuild or Google’s Cloud Build. Relying on them sacrifices the ability to orchestrate your entire infrastructure seamlessly, necessitating manual intervention or bespoke tools to manage the resulting complexities.

The CI/CD solution is straightforward, and I won't dwell on it extensively. ArgoCD emerges as the undisputed champion in this space for deployments and excels even further for multi-cloud orchestration. Leveraging GitOps and declarative practices, ArgoCD ensures uniform code deployment across all clusters, abstracting away from any specific cloud vendor. The tooling is centralized, controlled directly from source control, and boasts multi-cluster features for added flexibility.

Link to Argo Docs

Monitoring and Alerting: Corralling the Metrics Menagerie

Moving on to monitoring and alerting, the challenges echo those faced in CI/CD. Now, the focus is on externalizing metrics, dashboards, alerts, and logs. Picture this: during a production outage, you are tasked with finding the app in one of your clusters, finding the cloud the app resides on and then debug the app with partial metrics and logs. The last thing you want is to scramble to correlate issues scattered across AWS, GCP, and Azure metrics. The pitfall of using vendor-specific tools is a loss of visibility into two-thirds of your fleet.

The silver lining lies in reclaiming control through data externalization and the adoption of third-party tooling. Leading the charge in this space, especially for Kubernetes-based workloads, are Datadog, Grafana, and Dynatrace. These platforms empower you to centralize your monitoring efforts, offering a unified view beyond the confines of individual cloud providers.

While the aforementioned tools are prominent players, the landscape is rich with alternatives. If you have any favorites or recommendations, feel free to share them in the comments below. 

The journey into the multi-cloud Kubernetes realm is challenging, but armed with the right tools and strategies, you're well on your way to mastering avoiding the worst challenges for multi-cloud. Come back next week for more practical tips.

Next topic is cluster management,

which will be another paradigm shift from single cluster or single cloud management options.