Case Study

Cloud-based Automation Improves Data Quality and Time Savings

challenge

Manual Processes Needed Upgrading

Our client is a managed service provider (MSP) who delivers complete hybrid IT solutions, from mainframe to Cloud, tailored to each client’s journey. They have a business unit and team that manages the data for their IBM and IBM ISV TADz reporting systems. Our client had been using a data tech team to fill out a half dozen spreadsheets to plan upgrades and build a roadmap for the technicians. None of this was automated and had to be completed each week. This led to their need for a unique partner with experience in both product design leadership and AWS architecture. Having a partner of this kind would enable our client to collaboratively build an application that would help get rid of these time-consuming manual processes.

solution

A Serverless Application Using Angular and AWS Tools

Utilizing our client’s reference application architecture within AWS, our joint team deployed a serverless application within a new AWS Account. We built an Angular application served via S3 and CloudFront using AWS Edge functions for authentication redirects to AWS Cognito and SPA router deep-linking. We deployed AWS Lambda functions, an OpenAPI 3 specification file with Amazon extensions and dozens of VTL mapping templates to integrate with DynamoDB to serve the API backend. We utilized a CQRS architecture model with a separate event-logging database in DynamoDB. From there, we utilized DynamoDB streams to build view models in a separate DynamoDB table to serve the read only endpoints of the application API. This provides the fastest possible writes using versioning to support optimistic concurrency and event sourcing for fast iteration on data models, as demanded by the client. It also provided real-time report generation. We utilized AWS CloudWatch for monitoring and integrating Bitbucket with AWS CodePipeline to build, deploy and test the application using AWS CodeBuild, AWS CodeDeploy, S3 and AWS CloudFormation. Finally, we used AWS CloudFormation to enable the team to quickly deploy feature environments for experimentation, A/B testing and development environments.

results

Improved Data Quality and Hundreds of Hours Saved

Our client’s cumbersome spreadsheets, which had a catalog of hundreds of clients and thousands of software versions, were consolidated into an application leveraging search and data validation, saving hundreds of hours per year as well as reducing staff burnout. We were able to provide them with “what if” scenarios that will allow our client to offer their clients data-driven consulting level advice that otherwise would have been a near impossible manual process. This improved data quality helped them to better serve their clients and improve their brand as a leading Mainframe MSP.

 

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