Introduction:

SD Tech, digital marketing firm, built a platform allowing marketers to display advertising in front of potential customers, evaluate the result, and optimize their media buying process.

Our client was looking to focus on the automation and optimization of the advertising process, as automating infrastructure deployment was crucial to maintain efficiency and consistency. They setup a CI/CD pipeline using GitHub Actions, Terraform on AWS automated their infrastructure as code for a Django-based advertisement application.

This case study explores why and how they adopted core DevOps principle, to achieve faster delivery, improving reliability, scalability, and operational efficiency.

Key Challenges: Overcoming Deployment and Scalability

SD Tech faced significant challenges including complexity of deploying across multiple environments which hindered the application’s overall operational efficiency. The primary challenges included:

• Storage Limitations: The application lacked persistent storage for images, making it vulnerable to data loss and scalability issues.
• Complex Multi-Environment Deployment: The architecture required a flexible and cost-effective solution to support multiple environments without compromising performance or security.
• Scalability and Cost Issues: The application struggled to handle fluctuating workloads efficiently, leading to performance degradation. This inability to dynamically adapt to varying demands led to suboptimal resource utilization and increased operational costs.

The Adex Solution: Infrastructure Enhancement with AWS for Scalability and Reliability

To overcome the limitations of SDTech's existing infrastructure, we implemented a robust- cloud bases highly solution leveraging Amazon Web Services (AWS). This comprehensive approach has significantly improved the application's scalability, reliability, and overall performance.

Key Improvements:

Persistent Storage with Amazon S3: We integrated Amazon S3 to store image assets independently of the application servers. This ensures data durability and accessibility, eliminating the risk of data loss during server restarts or failures.

Multi-Environment Deployment: We implemented a well- defined multi-environment setup, separating development and production environments to facilitate controlled development, testing, and deployment processes without compromising stability.

Scalability with Auto Scaling Groups: To dynamically adjust to the fluctuating workload, we integrated Auto Scaling Groups. This ensures the optimal performance as well as adjustment of the number of server instances based on real-time traffic demands.

AWS Services Utilized:

AWS Identity and Access Management (IAM): Securely manages access to AWS resources and services.

Amazon S3: Provides durable and scalable object storage for application artifacts and static assets.

AWS Auto Scaling: Automatically scales infrastructure to meet demand.

Amazon CloudWatch: Monitors and logs application and infrastructure performance.

Amazon Relational Database Service (RDS): Delivers a managed relational database service for data persistence and scalability.

By leveraging these AWS services, we have significantly enhanced SDTech's infrastructure, providing a reliable, scalable, and cost-effective solution.

Benefits

Our solution offered a comprehensive approach to address SDTech's deployment challenges, resulting in significant benefits:

• Faster Deployment: Automated CI/CD pipeline reduces deployment time, allowing frequent releases within minutes.
• Scalability & Performance: Auto Scaling adjusts resources based on demand, ensuring high performance and cost efficiency.
• Cost Efficiency: Auto Scaling and isolated environments optimize resource use, reducing unnecessary costs.
• Improved Monitoring: CloudWatch and GitHub Actions provide real-time monitoring, error tracking, and incident response.
• Developer-Friendly: GitHub-based deployment streamlines workflows and supports automated testing.
• Business Agility: Faster time-to-market for new features enhances agility and customer satisfaction.

Results

The adoption of the GitHub Actions CI/CD pipeline, integrated with Terraform and AWS services, delivered measurable improvements across multiple performance metrics:

• Lead time – The deployment takes 2 to 3 minutes after committing new changes in the repository. The reflection on the changes takes around 5 to 10 minutes.
• Deploy frequency – The organization achieves a deploy frequency of 10 to 15 production deployments per week. This high frequency is made possible by the streamlined CI/CD pipeline, which allows the team to commit new changes and see them live within minutes.
• Mean time between failure (MTBF) – In this organization, the Mean Time Between Failure (MTBF) averages around 30 hours, indicating that the CI/CD pipelines are generally stable.
• Mean time to recover (MTTR) – The Mean Time to Recover (MTTR) is impressively low, with the organization averaging 5 to 10 minutes to resolve a failed pipeline and initiate the next successful deployment.

Conclusion:

Implementing CI/CD with GitHub Actions on AWS has greatly enhanced the deployment efficiency, security, and reliability of SDTech's deployment and release process. By using GitHub's OIDC provider and AWS services, the team achieved faster cycles, better security, and reduced downtime, ensuring continuous application delivery.