Since 1976, this large, global financial institution has been serving customers with innovative applications in insurance, banking, and investments. As technology adoption in the financial sector grew, their IT department grew right along with it. Not only was this team maintaining the company’s applications and hardware, but they were also developing applications in house.
Confident that the public cloud had now reached a level of maturity where their data security and regulatory requirements could be met, the organization decided it was time to migrate their workloads to the cloud. Their ultimate goal was to lower costs, but they also felt the move to the cloud would lessen some of the strain on their IT department so they could focus more on the business’s objectives and on serving customers.
With an IT department that was already stretched thin, the organization needed 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 the organization’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 they wouldn’t be overpaying for cloud resources. Running comparable workloads, AWS’s 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. This customer 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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