This tutorial demonstrates how to optimize a brokerage application using Redis's JSON data structure and enhanced query capabilities. A brokerage's success is tied to its investors' engagement, and creating better investor engagement leads to more assets under management, trades, and fees/commissions. The application faces scalability and performance challenges when trading activity surges, requiring high availability, low latency, strong consistency, scalability, security, and consistent performance. Redis Enterprise supports JSON, graph, and time series data structures, offering features like Active-Active Geo-Distribution to meet demanding needs. A sample implementation models brokerage entities using JSON, storing and retrieving securities portfolios with Redis indexing. The application can retrieve security lots owned by an account, filter views, and calculate total quantities or average cost prices of securities. Redis Enterprise provides full support for JSON, real-time indexing, fast access, and atomic operations for JSON values, outperforming competitors in metrics like latency and read-write throughput.