Automating SaaS Application Build and Deployment Process on Kubernetes with CI/CD Pipelines using the AWS Code Commit, AWS Code Pipeline and AWS Code Build
HumanGov SaaS
In my latest project, I delved into cloud-based service automation, focusing my efforts on the HumanGov SaaS application. I aimed to refine the build and deployment processes using Kubernetes, coupled with the robust capabilities of AWS services for continuous integration and delivery.
Project Overview
1. Setting Up AWS CodeCommit
The project commenced with the configuration of AWS CodeCommit. This initial step was critical, laying the foundation for secure source code management. It was vital to adhere to version control best practices to maintain code integrity throughout the project’s lifecycle.
2. Implementing AWS CodeBuild
Next, I integrated AWS CodeBuild to automate the build process. This service was instrumental in compiling the code, running tests, and generating deployable artefacts of the HumanGov application. This step was crucial for ensuring a seamless build process within the AWS ecosystem.
3. Orchestrating with Kubernetes
Kubernetes played a pivotal role in this project. I configured Kubernetes clusters to host and manage the containerised HumanGov application. This involved defining deployments that dictate the operational and scaling parameters of the application.
4. Establishing CI/CD with AWS CodePipeline
Creating a CI/CD pipeline using AWS CodePipeline was a central aspect of this project. This pipeline automated the release process, automatically ensuring every code change underwent build, test, and deployment processes, targeting the Kubernetes environment.
5. Containerisation with Docker
Docker was employed for the containerisation of the HumanGov application. This step involved packaging the application into containers, ensuring consistency across various development and staging environments.
6. Utilising AWS Elastic Container Registry (ECR)
Once containerised, the Docker images were pushed to the AWS Elastic Container Registry (ECR) for secure storage and management. This repository was essential for maintaining the readiness of the images for deployment.
7. Automating Deployments on Kubernetes
The final stage of the project was to set up automated deployments on Kubernetes. This ensured that the application was consistently deployed to the cluster, managing updates and maintaining the application’s desired state with minimal downtime.
Key Takeaways and Conclusion
This project was a technical undertaking and a significant learning experience. It reinforced my understanding of AWS services, the advantages of Kubernetes in managing containerised applications, and the efficiency gains from automating build and deployment pipelines.
One of the most profound takeaways from this project was the realisation of how automation in cloud computing can dramatically enhance the speed and reliability of software deployment. The experience gained from configuring and managing these advanced tools has solidified my skills in cloud and DevOps, preparing me for upcoming challenges in the ever-evolving tech landscape.
In conclusion, this project was a testament to the power of cloud technologies and automation. It streamlined the deployment process for the HumanGov application and provided valuable insights into the synergy between cloud services, container orchestration, and CI/CD pipelines. As I reflect on this journey, I am confident that the skills and knowledge I’ve acquired will be pivotal in my pursuit of becoming a Cloud Engineer, Platform Engineer, or DevOps Engineer.
