GitOps the Hard Way, with Argo CD. This item is unavailable.GitOps the Hard Way, with Argo CD

Most Argo CD tutorials leave you with a demo: a cluster someone else provisioned, a sample guestbook app, one argocd app sync, and a green checkmark.

This book takes the other route. You provision real servers, stand up a real cluster, ship a real application, and wire it to Git so that every commit reconciles onto the cluster with no one running a deploy by hand. Nothing is pre-baked, you will run every command, apply every manifest, and read every error, because the friction is the point. The steps a hosted demo hides from you are exactly the ones that teach you how Argo CD behaves when something breaks.

What you'll learn

By the end of this book, you will have built a full GitOps setup from nothing and operated it, not read about it. Concretely, you will be able to:

• Explain what Argo CD does and does not do, and trace a change from a Git commit through the reconciliation loop to a running resource on the cluster.

• Name the parts that make up Argo CD, the API server, application controller, repo-server, Redis, Dex, the ApplicationSet controller, and say what breaks when each one is unhealthy.

• Provision real infrastructure from scratch: stand up servers with Terraform, bootstrap a Kubernetes cluster, and deploy an application onto it, so Argo CD has something real to manage.

• Install Argo CD and choose deliberately between the full, core, and custom installs, then log in from the CLI and register additional clusters.

• Create your first Application from the argocd CLI, then redefine the exact same setup as declarative manifests you keep in Git, including the repository credentials.

• Read an Application manifest field by field, and configure sync policy on purpose: automated sync, self-healing, pruning and its exceptions, retries and backoff, ignoreDifferences, namespace creation, and the sync options that change how Argo CD applies resources.

• Decide what happens to your running workloads when an Application is deleted, and set finalizers accordingly.

• Run your own code around a sync with resource hooks and sync phases (PreSync, Sync, PostSync), and order resources within a sync using sync waves.

• Create a local user and grant rights with RBAC, scoping one teammate to exactly one project, and verify the grant resolved instead of hoping it did.

• Package an application as a Helm chart and have Argo CD manage it as a versioned release, passing values and tracking what is deployed.

• Generate Applications from templates with ApplicationSets, producing many Applications from one definition across namespaces or clusters.

• Build a multi-branch pipeline that gives every Git branch/revision its own live environment, so a push creates a deployment and a merge or delete tears it down.

• And more!

You finish with a working artifact; an automated GitOps pipeline you assembled yourself, running on infrastructure you provisioned, that you can explain piece by piece.

Who it's for

Engineers who already know Kubernetes and Git and now want to run Argo CD for real: platform and DevOps engineers, SREs, security engineers, consultants standing up GitOps for clients, and anyone who inherited an Argo CD install and needs the black box to open. If you can read a Deployment and open a pull request without looking it up, you're ready.

You will install it, define and sync Applications, control sync behavior, hooks, and waves, lock down access with projects and RBAC, package with Helm, scale with ApplicationSets, and automate per-branch delivery. Each chapter ends with something running you built yourself.

The companion kit

Every code listing, full-color diagram, and screenshot lives in a companion kit you get with the book.

Aymen El Amri is a software engineer, author, and founder of FAUN.dev, a platform and newsletter network serving hundreds of thousands of developers across DevOps, cloud native, Kubernetes, and applied AI. He writes practical, hands-on technical books for engineers, covering Docker, Kubernetes, Ansible, AWX, Python, and large language models. His work focuses on turning complex infrastructure and AI topics into clear, example-driven guides that practitioners can apply immediately. More at FAUN.dev.