Overview:
With $ 122.7B assets under management, one of the world’s leading real asset investment managers offers a platform that gives their institutional clients access to real estate and infrastructure in the Americas, Europe, and the Asia Pacific. The platform is widely used; however, it had some issues that Ksquare needed to figure out and fix.
Challenge:
The problem with this world-class investment platform was that it was in a state of decline because of the large number of bugs being reported regularly. Our initial assessment showed a codebase that was highly complex and poorly designed. As a result of our analysis, we determined the current code was too complex, had no unit test coverage, and tons of code duplication with no forethought regarding the reuse of code.
With a complex and buggy system, there is no room for growth, and over time the platform would become inoperable.
Solution:
In addition to our technical documentation, we put together a small proof of concept replicating the current look and feel of the application and highlighting the areas where we could minimize duplication of code and component reuse. An entire rewrite of all web application functionality was our goal.
The entire build and deployment process was reimplemented using Docker containers and AWS ECR as a home for all Docker team’s images. This allowed us to migrate off AWS EC2 and migrate to the company’s internal Mesosphere cluster. UI automation testing became a reality and could be done against any published version of the codebase.
Results:
In the end, our client had a seamless and streamlined release process between environments and a strategic codebase with component and code reuse across the application and organization as a whole.
Our client went from using a complicated buggy system to a well-thought-out, manageable, and scalable investment forecasting and financial projection tool that will help them continue being one of the largest real estate services companies.
1. Quick turnaround time for newly implemented features.
2. Accurate property-related investment forecasting and projections.
3. Minimized the number of reported bugs and data inaccuracies.
