Background:
IBM® InfoSphere® DataStage® is a leading ETL Platform that extracts, transforms, and loads data. IBM offers this platform to engage its Business Clients in extracting value from their big data. IBM was preparing to launch this platform to work with both AWS and Azure platforms with differing workloads.
The Problem:
IBM needed certainty that its InfoSphere DataStage platform was cloud-ready as this Massively Parallel Platform (MPP) system needed high network performance. The goal was to identify the optimal cloud footprint for various InfoSphere DataStage workloads in AWS & Azure.
The Plan:
QPAIR packaged and deployed IBM InfoSphere DataStage in Kubernetes clusters across major cloud platforms. By using their cloud expertise on AWS & Azure, QPAIR ran multiple tests and benchmarked the application across both cloud providers. The results were reported back to IBM with the best practices to deploy on the multi-cloud environment. IBM used the reports from the benchmarking and optimization exercise to generate best practice guidelines for its customers.
Success Story:
QPAIR initiated tests and benchmarked multiple instances using their in-house multi-cloud benchmarking tool on both AWS & Azure. QPAIR ran several TPC-DI benchmark tests, an industry-standard way to measure performance. They were able to come to a conclusion that found the ideal Performance vs Cost mix for the varying needs of the application. Leveraging this data IBM was able to confidently recommend guidelines according to the needs of their clients and the needs of the application.
