Vivek Yadav, an engineering manager at Stripe, explains how his team developed a testing system that utilizes years of data to ensure the reliability of their payment services. The primary challenge they faced involved verifying that their system could handle the complexities of business logic changes without impacting existing functionality. To tackle this, they leveraged Apache Spark, a framework that allows for efficient processing of large datasets. By using Spark, they could run extensive tests on historical data, significantly speeding up the testing process that would otherwise take an impractical amount of time. The approach involved treating their service logic as a library that could interact with various input and output systems. This modular design made it possible to run the same code in different environments, including Spark, which operates on bulk data rather than single requests. This method allows Stripe to process years' worth of transactions in a fraction of the time it would take with traditional testing methods. Yadav emphasizes the importance of avoiding personal identifiable information (PII) in test data and maintaining a separate testing environment that mimics production without risk. Overall, Yadav's insights highlight a practical application of big data processing in a highly regulated industry. The ability to conduct thorough regression testing with historical data not only enhances reliability but also supports ongoing system migrations and improvements, all while ensuring compliance with data protection standards.