Serverless RDBMSs, such as AWS Aurora, offer a significant advancement in handling analytics on production RDBMS instances by decoupling processing power from storage, enabling scalable infrastructure and simultaneous OLTP and OLAP workloads. In a benchmark test, an unoptimized dataset of 100 million rows queried on Serverless MySQL Aurora took 176 seconds, with potential reductions to sub-200ms using multi-stage querying. The test involved generating a large dataset from the Sakila Sample Database, utilizing Cube.js for caching and pre-aggregation to improve query performance, and optimizing with indexing and pre-aggregation strategies that effectively reduced query processing times. While Serverless Aurora MySQL can manage extensive workloads, limitations in workload routing algorithms pose challenges, suggesting that a provisioned Aurora MySQL with read replicas or Parallel Query might be more suitable for production environments to efficiently handle OLTP and OLAP workloads.