Google Cloud DevOps - Learn to create CI/CD pipelines in GCP

From this lecture we will start our journey to learn GCP CI/CD. And in this particular lesson you will learn about the term CI/CD (Continuous Integration/Continuous Delivery/Continuous Deployment) in general. Understanding these core DevOps concepts is essential for building robust Google Cloud DevOps workflows and mastering automated CI CD pipelines.

In the lecture we will see what are the stages that a software has to go through (in general) during its development cycle to build a high-quality software.

After learning about Software Development Lifecycle. In this class we will see how companies used to build software/any other applications without adapting CI/CD (Continuous Integration/Continuous Delivery, Development) work culture.

Here we will discuss about the drawbacks of developing a software without embodying CI/CD practices. Understanding the limitations of the conventional approach highlights why modern CI/CD DevOps workflows are so valuable for faster, automated, and reliable software delivery.

After learning the issues and drawbacks of conventional SDLC, in this lecture we will learn how a company can adapt CI/CD work culture and also will see how adapting CI/CD work culture and DevOps standards into SDLC helps to eradicate the drawbacks we discussed.

In this lecture, you’ll learn how to set up your Google Cloud Platform (GCP) account.

In this lecture, you’ll learn how to create and configure a new Google Cloud Platform (GCP) project, which acts as the central workspace for all your DevOps resources and CI/CD pipelines. You will get a guided tour of the GCP Console. This overview will help you navigate the GCP UI.

In this lecture, we'll setup GitHub repository —a fully managed Git repository service. Version control is a critical part of DevOps, and having your code in a central Git-based repository ensures seamless integration with tools like Cloud Build. You'll learn how to set up and access your repository, laying the groundwork for automated builds, tests, and deployments.

In this lecture, you'll get an introduction to Cloud Build, Google Cloud’s fully managed service for implementing GCP CI/CD pipelines. Understand how Cloud Build fits into the broader Google Cloud DevOps ecosystem.

In this lecture, you'll explore the structure and key directives of the cloudbuild.yaml file, the core of any Cloud Build pipeline. We’ll break down how this configuration defines your build steps. By understanding how to structure this file, you'll gain better control over your GCP CI CD workflows and strengthen your Google Cloud DevOps skills for real-world projects.

In this lecture, we’ll write our first basic cloudbuild.yaml file.

In this lecture, you'll create your first Cloud Build trigger to automatically run the cloudbuild.yaml pipeline created in the previous lecture. You'll learn how to configure trigger conditions—such as on code push or branch changes—and link your source repository to the build process — GCP CI/CD pipeline.

In this class we will go through the pipeline execution log details and will explore the various steps a Cloud Build CI CD pipeline goes through to complete the job defined in a pipeline.

In this lecture, you'll get a foundational understanding of what artifacts are in the context of CI/CD pipelines. Artifacts are the key concept in building reliable and reusable assets within your GCP DevOps workflow.

In this lecture, we explore the different artifact repositories available for storing and managing your build outputs. We'll also cover supported formats (like Docker, Maven, and npm). You'll also learn the key differences between Artifact Registry and Container Registry.

In this lecture, we will build a simple Flask-based Python application that will be deployed through the CI/CD pipeline.

In this lecture, we set up Artifact Registry, which will be used to store Docker images and other build artifacts generated by our CI/CD pipeline. You’ll learn how to create a new registry in Google Cloud, choose the appropriate repository format, so Cloud Build can push container images to it.

In this lecture, we configure our Cloud Build pipeline to push Docker images to the Artifact Registry. You’ll learn how to define the appropriate build step in the cloudbuild.yaml file.

In this lecture, we introduce Cloud Run, Google Cloud’s fully managed serverless platform for deploying containerized applications. Cloud Run fits into the GCP CI/CD and Google Cloud DevOps ecosystem, enabling fast, scalable, and cost-efficient deployment of Docker-based services directly from the pipeline.

In this lecture, we extend our CI/CD pipeline by adding a deployment step to Cloud Run within the cloudbuild.yaml file. You’ll learn how to configure the deployment using the gcloud run deploy command for the automated delivery of your application to the Cloud Run platform. This brings full automation from code push to live deployment.

In this lecture, we create a Cloud Build trigger to automate the execution of our pipeline on every code push to the repository.

In this lecture, we get an introductory overview of Cloud Storage and demonstrate how to use it for storing non-image artifacts generated by our CI/CD pipeline. Using a compiled Go application as an example, we configure Cloud Build to output executable artifacts and push them to a designated Cloud Storage bucket.

In this lecture, you’ll continue working with Cloud Storage to manage and verify the artifacts stored from our Google Cloud CI/CD pipeline.

In this lecture, we explore the two main approaches to storing image artifacts in Google Cloud: using the 'images' tag in the cloudbuild.yaml file and with the 'docker push' command.

In this lecture, you’ll get an introduction to substitution variables in Cloud Build, a powerful feature for making your DevOps pipelines more flexible and dynamic.

We’ll explore predefined substitution variables available in Cloud Build. Learn how to leverage these built-in values — like commit SHA, branch name, or build ID — to make a GCP CI/CD pipelines smarter and more adaptable.

In this lecture, you’ll learn how to create and use user-defined substitution variables in Cloud Build. Discover how to pass custom values into your cloudbuild.yaml file to make your GCP CI/CD pipelines more dynamic and configurable.

In this lecture, you’ll learn about dynamic substitutions in Cloud Build.

In this lecture, you’ll learn how to use substitution variables effectively within script-type build steps in Cloud Build.

In this lecture, you’ll learn how to pass substitution variables to your scripts in Cloud Build by manually mapping them to environment variables. You’ll see how to define these environment variables at the step level using the env field in your cloudbuild.yaml file.

In this lecture, you’ll learn how to automatically map substitution variables to environment variables.

In this lecture, you’ll get introduced to GCP Secret Manager, Google Cloud’s secure solution for storing and managing sensitive information like API keys, passwords, and tokens. Learn how Secret Manager integrates with your GCP CI/CD pipelines, helping you implement secure and compliant Google Cloud DevOps workflows by keeping secrets out of your source code and build files.

In this lecture, you’ll learn how to write a cloudbuild.yaml file that securely logs in to Docker Hub or a container registry using credentials managed by GCP Secret Manager. This step ensures that your GCP CI/CD pipeline can authenticate and push images securely as part of your automated Google Cloud DevOps workflow.

In this lecture, you’ll learn how to create and manage secrets in Google Cloud Secret Manager.

In this lecture, you’ll learn how to securely use secrets in your cloudbuild.yaml file. Discover how to reference and inject sensitive information — like API keys or Docker credentials — into your GCP CI/CD pipeline steps while keeping them safe and out of your source code.

In this lecture, you’ll extend your cloudbuild.yaml to build a Docker container image and push it to your Docker Hub account, using the credentials and login step you configured earlier.

In this lecture, you’ll learn how to create a scheduled trigger in Cloud Build to run your GCP CI/CD pipeline automatically at defined intervals — without waiting for a code push. Discover how to configure schedules using Cloud Scheduler, ensuring your builds run regularly for tasks like nightly deployments, automated tests, or periodic updates. This is an essential technique for automating maintenance in your Google Cloud DevOps workflows.

In this lecture, you’ll learn how to skip a Cloud Build trigger under certain conditions, such as when committing minor changes like documentation updates. You’ll see how to configure your GCP CI/CD pipeline to ignore specific commits using patterns or commit messages, helping you optimize build costs and time.

In this lecture, you’ll learn how to use the waitFor directive in your cloudbuild.yaml file to control the execution order of build steps in Cloud Build. Mastering waitFor lets you define dependencies between steps, ensuring that your GCP CI/CD pipeline runs tasks in the right sequence for reliable, predictable builds — a key best practice in Google Cloud DevOps workflows.

In this lecture, you’ll learn how to use the timeout option in your cloudbuild.yaml file to set a maximum execution time for your Cloud Build pipeline or individual steps. This helps prevent runaway builds, control costs, and ensure that your GCP CI/CD pipeline behaves reliably under various conditions.

In this lecture, you’ll explore the different audit logging categories available for Cloud Build in Google Cloud Platform like Admin Activity, Data Access, and System Event logs to monitor and troubleshoot your GCP CI/CD pipelines.

In this lecture, you’ll learn how to store and manage Cloud Build logs in a Google Cloud Storage bucket. Discover how to configure your cloudbuild.yaml to automatically export build logs for long-term storage, auditing, or troubleshooting.

In this lecture, you’ll learn about the defaultLogsBucketBehavior field in Cloud Build. Understand how this configuration controls whether build logs are stored in a user-specified Cloud Storage bucket or the default bucket managed by Google Cloud. This option helps you fine-tune log storage for your GCP CI/CD pipelines, improving visibility, compliance, and overall Google Cloud DevOps practices.