AWS, Cloud Computing

4 Mins Read

Blue Ridge Global – A CICD Implementation Case Study

The client Blue Ridge, blends the disciplines of supply chain demand planning and pricing under a fully configurable cloud-based platform. The Blue Ridge planning and pricing platform gives business leaders app simplicity that uniquely integrates data science-rich inventory forecasting capabilities with price optimization insights.

Blue Ridge empowers wholesale distributors, specialty retailers, and discrete manufacturers with the capability to adapt to the market, product, and competitive challenges by efficiently and effectively managing an ever-volatile supply chain.

The customer faced several challenges to scale their business and approached CloudThat as a DevOps expert to provide them with the end-to-end implementation of continuous integration and continuous delivery partner.

The client’s objective is to migrate applications from server-based deployment to container-based deployment, automate the provisioning of the infrastructure on AWS, configure container repository, Set up CI/CD pipeline for deployment automation and fast releases, and many more.

Proposed Solution

After a careful analysis of the client’s IT infrastructure and organizational structure, CloudThat’s DevOps team proposed the following solution:

AWS Services used as part of the Solution

Third-party Tools used

Architectural Diagram

  1. CICD Diagram
    CICD Pipeline

  2. Flow Diagram
    Flow Diagram

  3. VPC design
    VPC design

Outcome and Results

  1. The startup time and performance of applications were considerably improved since they are running on pods in the cluster. (Earlier startup was 5 mins but came down to 5-6 seconds)
  2. Downtime (tenant onboarding time) was reduced since RollingUpdate strategy was followed in the Kubernetes Deployment resource. (From 3-4 mins to less than a min because the Docker image will be containing the new tenant)
  3. Operational overhead was reduced in terms of
    1. Automated infrastructure provisioning using eksctl and CloudFormation
    2. The combination of HPA and cluster autoscaler facilitated smooth scaling of resources based on defined criteria
  4. CI/CD implementation and related DevOps best practices facilitated new feature releases and rollbacks with zero downtime and deployments spanning within minutes
  5. Resource (over and under-utilization) wastage was minimized, and the overall cost was brought down by making use of microservice-based architecture combined with HPA and Cluster Autoscaler by 10%
  6. Providing custom features through specific version updates to each tenant was made possible using a dedicated deployment design for each tenant
  7. Developers were able to view the application logs from DataDog in real-time and make appropriate code changes to resolve the same
  8. The multi-environment set up for the application deployment is made into effect which satisfies the given organizational SLAs and internal process framework which helps business growth

Outcome Metrics

About CloudThat

We here at CloudThat are the official AWS (Amazon Web Services) Advanced Consulting Partner and Training partner and Microsoft gold partner, helping people develop knowledge on cloud and help their businesses aim for higher goals using best in industry cloud computing practices and expertise. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Our blogs, webinars, case studies, and white papers enable all the stakeholders in the cloud computing sphere.

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WRITTEN BY Prarthit Mehta

Prarthit Mehta is the Business Unit Head-Cloud Consulting at CloudThat. He is an AWS ambassador and has experience delivering solutions for customers from various industry domains. He also holds working experience in AWS and Big data platforms. He is an AWS Certified Architect - Professional and a certified Microsoft Azure Solutions Architect.



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