The question is broad, but that’s expected for systems operating at this scale. When I hear requirements like 1M+ cases per day, the first thing I challenge is whether “cases” are actually the right abstraction. In Pega there is a clear distinction between data and cases. Cases bring significant built‑in capabilities around lifecycle, auditability, locking, assignments, SLA, and governance. That overhead is essential for true case management, but if those capabilities are not being used deliberately, the cost can be substantial and will directly impact throughput and latency.
The second important point is that, at this level of volume and concurrency, this is no longer just casual business application development; it becomes an engineering problem. There is no single design pattern or best practice that neatly solves data access, caching, reporting, asynchronous processing, and database load at the same time. Each area introduces its own constraints, and optimizing one typically means accepting trade‑offs in another. It is best approached as an obstacle course, where each design decision clears one constraint while introducing the next.
This is also why performance testing cannot be treated as a final validation step. It has to be part of an iterative process, continuously informing design decisions as the application evolves. At this scale, assumptions that look reasonable on paper tend to break under real load, and only repeated testing and adjustment will reveal where the true bottlenecks are.