Case Study

A Demand Planning Software Company Speeds Up Time-to-Market & Optimizes Cost with DevOps Practices

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IT services and consulting


DevOps, AWS


Continuous integration and deployment through DevOps best practices on AWS

About 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 provides 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 effectively managing an ever-volatile supply chain


Application start-up time

Application start-up time


Reduced from 4 minutes to less than 1 minute

Overall cost

Reduced by 10%

The Challenge

  • Slow-release cycles take weeks together to release new features. 
  • Uncertainty regarding rapid time to market and faster release cycle activities. 
  • Reduction required in operational overhead while keeping the release cycle rolling at a minimal cost and lacked support of a dedicated technical team. Activities. 
  • Lack of transparency in resolving errors. 
  • Lack of smoother version updates and rollbacks resulting in improper version updates. 
  • Inability to access the real-time application logs from their existing monitoring setup using Datadog. 
  • Unavailability of continuous application deployment. 


  • Designed and set up an Elastic Kubernetes Service (EKS) cluster.
  • Configured both Linux and Windows nodes. Enabled cluster autoscaling. Configured AWS Load Balancer Controller and Nginx Ingress controller.
  • Containerized the .NET applications and deployed them to ECR and resolved application-related errors.
  • Deployed Highly available, scalable, fault-tolerant microservices to EKS for the specific namespaces.
  • Setup single deployment for login applications and exposed using an AWS application load balancer.
  • Setup dedicated Kubernetes Deployment resource per tenant for all other applications. Applications are exposed using cluster-IP service type.
  • Implemented highly available Nginx ingress backed by AWS NLB for traffic management. Further, configured host-based routing along with the ingress resource.
  • Accomplished HPA enablement based on CPU and memory consumptions.
  • Successfully implemented infrastructure and application monitoring with AWS CloudWatch and Datadog.
  • Integrated Datadog with EKS by ensuring container logs are accessible from Datadog Dashboard.
  • Successfully integrated TeamCity with BitBucket by configuring it with TeamCity project.
  • Integrated TeamCity with ECR by adding ECR connection in TeamCity project configuration and by adding docker support in build configuration.
  • Accomplished integration of Octopus with ECR by adding AWS ECR as an Octopus External Feed.
  • Integrated Octopus Deploy with EKS by adding EKS Cluster as a Kubernetes target. Create service accounts to configure the added Kubernetes target.

The Results

Fostered business growth through better implementation of the organizational SLA and internal process framework by setting up the client multi-environment for application deployment

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