The Sonar engineering team successfully removed a performance bottleneck and transformed the Processor service to make it scalable, resilient, and cost-effective while improving the user experience. They achieved this by cutting the file storage cost on SonarQube Cloud by 90 percent and extracting 3.4 TB of data from a relational database to a more suitable storage option. The team evaluated two main candidates for file storage: Amazon Simple Storage Service (Amazon S3) and Amazon Elastic File System (AWS EFS). After running a proof of concept, they validated their assumptions and gathered initial data before conducting a larger experiment. This experiment showed that Amazon S3 is the fastest option with the asynchronous client, but saving each file individually is costly due to high requests, impacting Amazon S3's pricing. However, by bundling files in archives, the cost of S3 can be reduced to 10 percent of EFS' cost. The team also realized the need to transform their architecture to fully leverage the new storage's potential, which led to a more efficient and scalable design. This transformation improved the platform's scalability, resilience, and user experience, while preparing it for future growth and improvements.