DevOps for Data Engineering
About our client
KPI’s client is a global manufacturer and distributor of scientific instrumentation, reagents and consumables, and software and services to healthcare, life science, and other laboratories in academia, government, and industry with annual revenues of $32 billion and a global team of more than 75,000 colleagues.
Customer Need / Business Driver
Our client’s deployment cycle is longer and error-prone with manual version control management, no automation in Infrastructure provisioning, and code deployment to non-pro or prod environments. This client was looking to optimize its current processes by improving code management, reducing deployment cycle timelines, and introducing automation.
Selection Process
The client selected KPI over multiple vendors through a rigorous RFP process. KPI’s expertise in delivering DevOps for AWS, Azure, and data engineering, in general, was the key differentiator. Also, KPI’s blended shore project execution model minimized cost and customer risk and also played a significant role in the decision-making process.
What KPI Delivered
- Designed and developed GitHub Repositories and folder structure for CAD supply chain project that can be extended to enterprise data platform for source code management
- Designed and developed GitHub branching strategy for improved team collaboration and stable and faster releases to non-prod and prod environments based on business use cases and best practices.
- Jenkins pipelines and jobs for auto code build, package, and deployment to non-prod environments.
Terraform templates to automate s3 bucket provision as Infrastructure as Code (IaC)
Technical Benefits
- Improved version control Management
- Continuous Integration Automation
- Infrastructure as Code automation
Business Benefits
- Visible faster delivery of new features and bug fixes
- More stable operating environments
- Improved Teams collaboration
- Improved code deployment process
- Superior customer experience
