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Revolutionising Cloud Infrastructure with AWS and Terraform

Revolutionizing Cloud Infrastructure with AWS and Terraform: A Case Study

Client Background:

The client, a rapidly expanding e-commerce company, found their cloud infrastructure becoming increasingly difficult to manage as their business grew. Their environment was complex, utilising a range of AWS services including Elastic Kubernetes Service (EKS), Route 53, Application Load Balancer (ALB), Elasticsearch, and EC2 instances. The manual approach to managing this infrastructure was leading to inconsistencies, operational inefficiencies, and increased risks.


The Challenge:

As the company's customer base and application demands grew, the manual management of their AWS infrastructure became untenable. They faced several key challenges:

  • Scalability: The need to rapidly scale infrastructure to meet fluctuating demands without downtime or performance degradation.

  • Consistency: Ensuring uniform deployments across multiple environments to reduce discrepancies and improve reliability.

  • Efficiency: Automating repetitive tasks to save time and reduce the risk of human error.

  • Resilience: Implementing high availability and disaster recovery measures to safeguard against outages and data loss.

  • Cost Management: Optimising resource usage to reduce operational costs, especially as the scale of operations increased.


The Atsky Solution:

To address these challenges, Atsky proposed a comprehensive shift to Infrastructure as Code (IaC) using Terraform and Helm, alongside the implementation of Karpenter for resource scaling within EKS. Our approach was designed to automate, streamline, and optimise the client’s AWS infrastructure management processes.



Key Elements of the Solution:

  • Terraform:

    • Complexity: Writing, planning, and deploying infrastructure as code (IaC) using Terraform required a deep understanding of the client’s existing architecture and the intricacies of AWS services. Each AWS resource—from VPCs and subnets to ALBs and EC2 instances—was meticulously defined in Terraform configuration files, ensuring that the infrastructure could be recreated consistently across environments.

    • Business Benefit: Terraform’s declarative approach provided a single source of truth for the client’s infrastructure, enabling consistent, repeatable deployments and reducing the risk of configuration drift. This level of automation significantly enhanced operational efficiency and reliability.

  • Helm:

    • Complexity: Helm was employed to manage Kubernetes applications on EKS, utilising Helm charts to simplify the deployment and management of complex, containerised applications. This involved customising Helm charts to fit the client’s specific requirements, ensuring that even the most complex Kubernetes applications could be deployed and upgraded with ease.

    • Business Benefit: Helm allowed the client to manage their Kubernetes applications more effectively, streamlining the deployment process and enabling easy rollbacks in case of issues, thereby minimising downtime and ensuring continuous delivery.

  • Karpenter:

    • Complexity: Implementing Karpenter to manage node scaling in EKS required careful integration with the existing Kubernetes setup. Karpenter dynamically scales EKS clusters based on workload demands, optimising resource usage in real-time.

    • Business Benefit: This automation not only improved the efficiency of resource management but also resulted in significant cost savings by ensuring that resources were only provisioned as needed, avoiding over-provisioning and reducing waste.

  • AWS Services Automation (Route 53, ALB, EC2, Elasticsearch):

    • Complexity: Automating the configuration and management of critical AWS services such as Route 53, ALB, EC2, and Elasticsearch involved writing and maintaining complex Terraform scripts that ensured these services were consistently and correctly deployed across multiple environments. For Elasticsearch, this also meant setting up clusters capable of handling large volumes of data efficiently.

    • Business Benefit: By automating these services, the client achieved a highly available, resilient infrastructure that could scale on demand and handle large data volumes efficiently. The automated processes also ensured that disaster recovery plans were integrated into the infrastructure, minimising downtime in case of an outage.



The Results:

  • Scalability:

    • The client was able to scale their infrastructure rapidly and efficiently to meet growing demand, thanks to the automated provisioning and management of AWS resources through Terraform and Karpenter. This ensured that their infrastructure could handle peak loads without manual intervention.

  • Efficiency:

    • Automating manual tasks with Terraform and Helm saved significant time for the client’s IT team, allowing them to focus on more strategic initiatives rather than routine maintenance. The reduction in manual intervention also minimised the risk of human error.

  • Consistency:

    • Infrastructure deployments became consistent and repeatable across all environments, reducing discrepancies and improving reliability. This consistency was crucial for maintaining high standards of performance and security.

  • Cost Savings:

    • Karpenter’s dynamic resource scaling resulted in significant cost savings by optimising the use of computing resources, ensuring that the client only paid for what they actually used, without compromising performance.

  • Resilience:

    • The infrastructure’s resilience was enhanced through automated disaster recovery setups and high availability configurations, ensuring minimal downtime and fast recovery in the event of failures.



Conclusion:

This project showcases Atsky’s proficiency in revolutionising cloud infrastructure management through the use of Infrastructure as Code (IaC) and advanced automation tools like Terraform, Helm, and Karpenter. By transitioning the client to a fully automated, scalable, and resilient cloud environment, we enabled them to meet their growing demands while optimising costs and improving operational efficiency.


Connect with us today to explore how we can help you revolutionise your infrastructure management and achieve similar results.

Power in Numbers

Deployment Time

In mins

Change Failure Rate

Near Zero

Recovery Time

Real Time

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Lead Time

Dynamic

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Release Cadence

Dynamic

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