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Case Study: SWBC Migrates 200+ Workloads to AWS



A Couple Buying a New House



SWBC wanted to migrate workloads from an on-premises data center to the public cloud to lessen the strain on IT and give them more time to focus on application development.


With an IT department that was already stretched thin, SWBC wanted a partner that could help them validate their cloud platform
choices, configure cloud instances correctly, and migrate their data securely. Before kicking off the migration project, WSM performed an analysis to validate SWBC’s choice of AWS as the preferred cloud environment. AWS met the technical standards, but they also performed a cost analysis against Azure to ensure SWBC wouldn’t be overpaying for cloud resources.

Running comparable workloads, AWS monthly costs came in 38% lower than Azure. “We don’t always see that kind of cost differential between AWS and Azure,” said Tina Wisbiski, WSM’S project lead. “A lot of it depends on the requirements of the organization and the workloads we’re migrating. SWBC already had a preference for AWS, but showing the project team the cost analysis removed any remaining doubts.”


The financial institution asked WSM to migrate a total of 475 servers to AWS. “In such a large migration, we group servers based on application dependencies so that we’re not trying to migrate everything at once,” said Wisbiski. “We have a tool we run against an organization’s systems to discover these dependencies so nothing gets missed.”

After analyzing dependencies, Wisbiski and her team grouped servers into 13 move groups. Then, they prioritized the less mission-critical environments, such as Dev & QA, to ensure those workloads will move smoothly into AWS. The team then applied the lessons learned from phase one to the migration of the environment the financial institution uses for their beta program. This served as a dry run for migrating the actual production workloads.

“By taking a phased approach we can uncover any potential issues that the client may not have been aware of,” said Wisbiski. “We can apply those learnings to the production cutovers to ensure a successful migration with the smallest maintenance window possible.”



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