> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Problem Statement Meeting with XYZ Team > This article explores DevOps prerequisites and demonstrates how GitLab CI/CD can streamline workflows for a software providers cloud migration and container strategy. In this article, we’ll explore the key DevOps prerequisites for a software provider and demonstrate how [GitLab CI/CD](https://docs.gitlab.com/ee/ci/) can address these requirements with a streamlined, scalable workflow. Dasher Technology offers a platform that connects data, applications, and devices across on-premises environments. Their R\&D team is investigating a cloud migration and container strategy—starting with a [Node.js](https://nodejs.org/) project and later extending to [Java](https://www.oracle.com/java/) and [Python](https://www.python.org/) applications. Alice leads the new DevOps team tasked with building the CI/CD pipeline from the ground up. Their multi-cloud architecture will use [Docker](https://www.docker.com/) for containerization and [Kubernetes](https://kubernetes.io/) for orchestration. Alice’s initial assessment uncovered several pain points: * No version control or branch management * Manual code integration and testing * Slow, unreliable test runs with low coverage * Infrequent merges that elevate release risk * Manual deployments to development, staging, and production To overcome these challenges, the team will implement a CI/CD pipeline consisting of: 1. Version control and collaboration on [GitLab](https://about.gitlab.com/) 2. Automated unit tests with code coverage 3. Docker image builds and registry pushes 4. Deployment to Kubernetes clusters 5. Automated integration tests before production rollout ![The image outlines the DevOps requirements for a team, featuring tasks like code integration, collaboration, testing, and deployment, with icons representing each step. It includes a section for automated IT processes and is copyrighted by KodeKloud.](https://kodekloud.com/kk-media/image/upload/v1752877313/notes-assets/images/GitLab-CICD-Architecting-Deploying-and-Optimizing-Pipelines-Problem-Statement-Meeting-with-XYZ-Team/devops-requirements-team-tasks.jpg) *** ## Evaluating CI/CD Tools The team evaluated several popular CI/CD platforms: | Tool | Pros | Cons | | -------------- | -------------------------------------------- | ------------------------------------------- | | Jenkins | Highly extensible, large plugin ecosystem | Complex setup, infrastructure management | | GitLab CI/CD | Integrated with GitLab, auto-scaling runners | Fewer community plugins compared to Jenkins | | Travis CI | Simple YAML-based configuration | Limited concurrency, slower enterprise tier | | CircleCI | Fast container performance, Docker support | Usage limits on free tier | | GitHub Actions | Native to GitHub, rich marketplace | Requires GitHub ecosystem | | Spinnaker | Advanced deployment strategies | Steep learning curve | | Bamboo | Tight Atlassian integration | Licensing costs, less community-driven | Alice initially chose Jenkins due to its maturity and active community. However, setting up and maintaining a Jenkins server for a single Node.js project proved time-consuming: * Provision virtual machines with sufficient CPU, memory, and disk * Install Java JDK, Jenkins, and required plugins * Configure firewall rules and security groups * Install and manage multiple Node.js versions and npm * Install Docker for container builds * Add Kubernetes tooling: `kubectl`, [Helm](https://helm.sh/) * Integrate external testing/reporting CLIs Extending this setup for Java or Python on AWS/Azure adds layers of complexity: * Java: Maven or Gradle * Python: virtualenv, pip * Cloud CLIs: [AWS CLI](https://aws.amazon.com/cli/), [Azure CLI](https://learn.microsoft.com/cli/azure/) * DevSecOps: vulnerability scanners like [Trivy](https://github.com/aquasecurity/trivy) and KubeSec ![The image lists traditional CI/CD tools and challenges, featuring icons for Java, Maven, Python, Azure, AWS CLI, Trivy, and Kubesec.](https://kodekloud.com/kk-media/image/upload/v1752877313/notes-assets/images/GitLab-CICD-Architecting-Deploying-and-Optimizing-Pipelines-Problem-Statement-Meeting-with-XYZ-Team/ci-cd-tools-challenges-icons.jpg) Managing your own CI/CD infrastructure means spending more time on setup and maintenance rather than on writing pipelines. Because many team members are new to this tool ecosystem, Alice needed a solution that would: * Require minimal infrastructure setup * Let the team focus on pipeline authoring * Provide built-in scalability and security ![The image outlines challenges of traditional CI/CD tools, highlighting the need for simple setup without extensive installations and focusing on pipeline development without infrastructure concerns. It includes a central icon labeled "Alice."](https://kodekloud.com/kk-media/image/upload/v1752877314/notes-assets/images/GitLab-CICD-Architecting-Deploying-and-Optimizing-Pipelines-Problem-Statement-Meeting-with-XYZ-Team/ci-cd-challenges-simple-setup.jpg) After evaluating multiple platforms, Alice selected **GitLab CI/CD**. In the following sections, we’ll build and optimize a GitLab CI/CD pipeline for a real-world Node.js application—covering: * Version control and merge request workflows * Automated testing and code coverage * Docker image creation and registry hosting * Kubernetes deployments and rollbacks * Integration testing and monitoring Let’s get started! ## Links and References * [GitLab CI/CD Documentation](https://docs.gitlab.com/ee/ci/) * [Docker Official Site](https://www.docker.com/) * [Kubernetes Documentation](https://kubernetes.io/docs/) * [Node.js Homepage](https://nodejs.org/) * [Java SE Downloads](https://www.oracle.com/java/technologies/javase-jdk-downloads.html) * [Python.org](https://www.python.org/)