The customer’s application allows them to optimize truck loads in a way that can be tweaked to take in many different configuration values and calculate an optimal way to distribute loads. Over the years, code quality issues have presented themselves, making the code more difficult to maintain. Additionally, the code for this application was written in a very early version of Java, causing it to lack some basic quality-of-life features such as generics, lambdas, and other language features. Because of this, some of the code is unwieldy, including very long-length methods and classes, accessing static variables throughout the code, and many other code smells. Therefore, with this tech stack, getting new developers and bringing them up to speed was challenging, and continuing work without changes exacerbated the issues.

To maintain its competitive advantage, the organization needed to migrate its data to Snowflake, a data warehouse, to help maintain accurate and efficient ways of reporting on the business. The previous analytical layer was hosted on Microsoft SQL Server; however, as the fintech grew and more data analysts were hired to perform analysis, the datasets and queries to produce data analytics became unwieldy. Not only did the Microsoft Analyst SQL Server begin to show latency and other shortcomings, but the data assets producing key performance metrics were being generated away from a central source, increasing silos and inconsistencies. Data was being managed by numerous people, in a variety of formats, creating distinct subject matter experts across data assets that were often producing duplicative metrics.

While the time at the client was short, Ippon introduced Spring Boot to portions of the application and established a blueprint for converting legacy code to a future state using Spring Boot. While it was challenging to estimate the exact amount of time saved from this project and progress towards a future state, it is important to recognize that with strong unit tests, and clean code, the customer can now more confidently implement changes, catch bugs, and deploy new features.

The customer works in the optimization space, originally starting with a PhD project and expanding into the business. They use mathematics to optimize and decide how best to distribute delivery loads while also considering many configuration parameters.