Why Engineers don't use Single Database | Database Per Service

A single column rename took down a service that nobody even deployed to. That's what happens when microservices share one database — and it's exactly why companies like Amazon and Netflix never do. This is the Database Per Service pattern: the one foundational rule behind real microservices. We start from a monolith, watch a shared database slowly create tight coupling, broken ownership, and tangled deployments — then fix all of it with one rule: every service owns its own database, and nobody touches anybody else's directly. We also cover the part most tutorials skip — the trade-offs you take on in return: the network-call problem, event-driven data duplication, eventual consistency, and the painful loss of the database JOIN (plus how API Composition and CQRS deal with it). If you're prepping for a system design interview, this is a must-know. ⏱️ Chapters 0:00 The 2 AM outage nobody caused 0:56 How it starts: the monolith 1:50 3 problems a shared database creates 3:25 The fix: Database Per Service 4:15 The one golden rule 5:15 The new problem this creates 5:56 Events & data duplication 6:55 4 big benefits 7:52 The painful trade-off: joins 8:49 How Amazon actually does it 9:34 Recap + interview tips 🔗 Watch first: the Shared Database anti-pattern (how the 2 AM outage happens) 🔔 Subscribe for visual system-design breakdowns — Saga & CQRS next. Connect with us on: Instagram:   / visualcoders   website: https://visualcoders.in/ facebook:   / 61587693743021   #systemdesign #microservices #databaseperservice #softwarearchitecture #backend