I recently started my journey into system design and data-intensive applications. This experience has completely changed how I look at software — especially around the hard tradeoffs of consistency, availability, and what happens when things go wrong at scale. Let me start with a scenario that surprised me when I first encountered it: Imagine you just inserted a record in your app. A millisecond later, a read on a different server returns the old data. How? Welcome to distributed systems. What is CAP Theorem? CAP stands for Consistency , Availability , and Partition Tolerance . The theorem states that a distributed system can only guarantee 2 out of these 3 properties at any given time. Consistency — Every read returns the most recent write, or an error. All nodes in the system see the same data at the same time. Availability — Every request gets a response (not an error), even if some nodes are down. The system is always up.…