Building a Polyglot Persistence Layer: When One Database Isn't Enough

You've optimized your PostgreSQL queries to death, but your analytics dashboard still crawls under load. Meanwhile, your document-heavy user profiles are forcing you into EAV anti-patterns, and your cache invalidation strategy has grown so Byzantine that onboarding engineers need a separate wiki page just to understand when data is fresh. You add another index, denormalize another table, and watch your write performance take another hit. At some point during your third consecutive sprint dedicated to "database performance improvements," you start to wonder: what if the database isn't the problem? It isn't. The problem is that you're using one database for everything. Relational databases are extraordinary tools, but they're not universal tools. PostgreSQL excels at transactional consistency and complex joins, but it buckles under analytical workloads that scan millions of rows. It handles structured data beautifully but turns schema evolution into a migration nightmare. MongoDB gives y