Database Design That Holds Up as the Data Grows

Relational for most products. If your data has relationships and you care about consistency — which covers orders, payments, inventory and user records — PostgreSQL is the sensible default, and it handles JSON when you need flexibility. Document databases suit genuinely variable shapes. Choosing NoSQL to avoid designing a schema usually means designing it later in application code, less reliably.

Most often a missing or wrong index, or a query pattern that was fine at a thousand rows and isn't at a million. We read the actual execution plans rather than guessing, because the expensive query is frequently not the one people suspect.

Usually yes, by splitting the change into steps that are each safe against live traffic — add the new structure, backfill in batches, switch reads over, then remove the old. It takes longer than a single migration but doesn't require taking the product offline.

Not once the reports get heavy. Analytical queries scan far more data than application queries and will compete for the same resources. A read replica is usually the cheapest fix; a separate analytical store is the answer when reporting becomes a product in its own right.

Yes — our telemedicine platform runs on PostgreSQL with encrypted data and role-based access, and database work runs through most of our backend engagements.