Part 7/12:
Transitioning from imperative programming to reactive programming paradigms allowed the platform to achieve almost three to four times higher request throughput, with a 50% reduction in operational costs. This modernization introduced a more reliable, scalable, and predictable data infrastructure capable of supporting the company's aggressive growth.
Key architectural changes included adopting Spark with tailored tuning, reducing redundancies, and removing dependencies on multiple languages, which streamlined data processing workflows and improved overall efficiency.
Addressing Engineering Challenges and Scaling Strategies
The legacy system faced several challenges:
Dependence on extensive marketing data processing systems
Limited partitioning capacity hindering scalability