GitLab CI/CD

Course Overview

The training is designed to help teams transition from Jenkins to GitLab CI/CD. It covers key concepts, tools, and practices necessary for a smooth migration and effectively utilizing GitLab’s CI/CD pipeline features. The course delves into configuring pipelines, managing runners, and optimizing workflows while exploring advanced topics like dynamic and nested pipelines, artifact management, and templating. Focusing on real-world applications, this training ensures a seamless shift from Jenkins to GitLab for efficient and secure software delivery.

Who is this course for

This course is ideal for DevOps engineers, software developers, and system administrators responsible for implementing or maintaining CI/CD pipelines. It’s a good fit for professionals transitioning from Jenkins to GitLab or seeking to optimize their CI/CD workflows for large or complex projects. A basic understanding of CI/CD concepts is recommended, but prior experience with GitLab is not required.

Key Benefits

  • Gain hands-on experience with GitLab CI/CD tools and techniques to enhance your development pipeline.

  • Improve career prospects by mastering GitLab CI/CD, a leading DevOps tool major companies use.

  • Learn strategies to optimize CI/CD pipelines, reducing build times and improving efficiency.
  • Develop skills to manage complex downstream, including dynamic, parent-child, and nested pipelines.

  • Understand best practices for migrating from Jenkins, making you more versatile in DevOps environments.

Learning Objectives

By the end of this course, you will be able to:

  • Understand the fundamental differences between Jenkins and GitLab CI/CD.
  • Set up and configure GitLab CI/CD pipelines tailored to your workflows.
  • Implement efficient caching, artifact management, and variable usage for optimized workflows.
  • Leverage templating and modularization to streamline and scale CI/CD pipelines across projects.
  • Build and manage advanced pipelines, including dynamic, nested, and child pipelines, to handle complex workflows.
  • Migrate existing Jenkins pipelines to GitLab, adapt them to GitLab's CI/CD architecture, and optimize them with advanced pipeline techniques.
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Format

  • Duration: 2 days
  • Format: In-person or Remote, Instructor-led training (ILT)
  • Course Difficulty: Intermediate
  • Experience Level: Intermediate
  • Hands-on Labs: Cloud based

Prerequisites

  • Linux fundamentals
  • Git fundamentals
  • CI/CD Fundamentals

Course contents

Day 1: GitLab CI/CD Overview

  • CI/CD Workflow: Overview of the CI/CD process and how GitLab facilitates it.
  • GitLab Overview: Key features and benefits over Jenkins.
  • GitLab Architecture: High-level architecture of GitLab and its components.
  • Jenkins vs. GitLab CI/CD: Benefits and trade-offs when migrating from Jenkins to GitLab.
  • GitLab Runners: Types (shared, group, specific) and their use cases.
  • Runner Executors: Shell, Docker, and Kubernetes executors for running jobs.

  • .gitlab-ci.yml Structure: Structure and syntax of the .gitlab-ci.yml file.
  • First Pipeline and Jobs: Your first GitLab CI/CD pipeline and configuring jobs, understanding their roles in the pipeline.
  • Stages: Organizing jobs into stages and understanding how stages are executed sequentially.
  • Dependencies: Managing job dependencies and controlling the order of execution.
  • Runner Selection: Using custom variables to assign runner tags in pipelines dynamically.
  • Triggering Pipelines: Configuring manual, scheduled, or event-based triggers.

  • Artifacts: Understanding artifacts in GitLab and how to store and share data between jobs.
  • Variables: Using predefined, custom, and secret variables to control pipeline behavior.
  • Scripts: Writing scripts for different stages to execute tasks.
  • Artifacts via JFrog: Storing and using build artifacts via JFrog Artifactory CLI.
  • Artifact Retention Policies: Managing long-term storage of artifacts.
  • Caches: Speeding up builds with caching strategies for dependencies and resources.

  • Advanced Job Configurations: Configuring parallel execution and managing job dependencies.
  • Complex Pipelines: Organizing complex pipelines with multiple stages and conditional logic.
  • Precise Job Control: Using only and except rules to control job execution.
  • Job Logs: Analyzing and troubleshooting job logs for better visibility and debugging.
  • Dynamic Jobs: Creating pipelines with dynamic jobs by leveraging needs for flexible workflows.
  • Best Practices: Managing and scaling complex CI/CD pipelines effectively.

Day 2: GitLab CI/CD Advanced

  • Build containers: Automate Docker builds in GitLab CI/CD.
  • Sign containers: Sign Docker images for integrity.
  • Scan containers: Use security scanners to check for vulnerabilities.
  • Multi-stage builds: Optimize container builds using multi-stage Dockerfiles.
  • Push to registries: Automatically push images to DockerHub, GitLab Container
  • Registry, etc.Container best practices: Apply best practices for secure container builds.

  • Parent-Child pipelines: Trigger and manage child pipelines with trigger.
  • Dynamic Pipelines: Create pipelines dynamically using Jinja templates.
  • Python Generation: Use Python scripts to automate pipeline creation and configuration.
  • Nested Pipelines: Configure and manage complex workflows with include.
  • Optimization: Improve pipeline performance and scalability with advanced techniques.
  • CMDB in pipelines: Create dynamic pipelines using your Configuration Management Database.

  • Jenkins Groovy: Review key concepts for migrating from Jenkins to GitLab.
  • Mapping to GitLab: Translate Jenkins Groovy steps into .gitlab-ci.yml syntax.
  • Conditional Logic: Convert Jenkins Groovy if/else statements into GitLab’s rules: only and except.
  • Declarative Pipelines: Adapt Jenkins declarative pipelines (stages and environment blocks) to GitLab's YAML format.
  • Shared Libraries: Replace Jenkins shared libraries with GitLab templates and include files.
  • Migration Best Practices: Ensure a smooth transition from Jenkins to GitLab with best practices and guidelines.

  • Pipeline Optimization: Techniques for optimizing CI/CD pipeline performance.
  • Efficiency Improvements: Improving pipeline efficiency using caching, parallelism, and artifacts.
  • Failure Handling: Implementing retries and failure-handling strategies for robust pipelines.
  • Modular Pipelines: Modularizing pipelines for easier maintenance and scalability.
  • Includes: Centralizing and reusing configuration across multiple projects with include.
  • Large Team Management: Best practices for managing complex pipelines in larger teams.

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