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CASE STUDY

Modernizing Complex Applications

Replacing an Underperforming Login System with a Robust, Scalable Service and No Downtime

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Technical Challenge

A leading entertainment company had an existing login system that was not meeting usage demands and was highly interdependent on existing services. They needed an account- and identity-management experience for every different brand under the enterprise organizational umbrella. The platform should be available online and offline for users to access their guest profiles anytime, anywhere. In addition, the platform should enable internal teams to drive personalized services, content and offers to its users.

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Why They Chose Nortal

The client needed a firm with extensive experience in platform modernization. It was critical that they experienced no downtime during the transition. Nortal’s CI/CD practice and experience with automation and breaking down monoliths into microservices ensured the company that they could make the move without losing critical user engagement.

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Nortal's Value & Challenges Overcame

Nortal’s first goal was to contain complexity so other internal organizations could build on the service — key to this effort was breaking down their monolith by using containers and microservices. In addition, these services could have no downtime or disruption. Nortal built a robust test suite to evaluate the core application. Upon passing, a series of performance tests were run with the underlying components to identify bottlenecks. The implementation continues to be solid, and is handling ongoing growth and seasonal spikes in-load without incident.

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Project Results

The application has been extremely reliable. The client has experienced no downtime since launch. The application met the goal of onboarding an internal business unit in a timely fashion while meeting stringent scalability and redundancy requirements. In addition, numerous other business units have onboarded to the application. The robust test suite allows for refactoring or adding new features with minimal to no risk of regressions.

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