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For decades, SQL was the default choice for almost every software system. Then, "Big Data" broke it. In this deep dive, we are exploring the history and technical trade-offs behind the SQL vs. NoSQL war. We reference Chapter 2 of Designing Data-Intensive Applications to move beyond the surface-level "flexible vs. strict" debate and understand the architectural reasons why NoSQL was invented in the first place. We break down the discussion into four key areas: The History: Why relational databases dominated for 40 years and why the explosion of data volume forced engineers to look for alternatives. The Trade-Off (ACID vs. Scaling): We explain why SQL's rigid consistency (ACID) makes it incredibly hard to Scale Out (Horizontal Scaling). NoSQL was born specifically to solve this distributed systems problem. The "Impedance Mismatch": A core concept from Martin Kleppmann’s book. Your application code is Object-Oriented (rich, nested data), but SQL tables are flat. We discuss how this friction slows down development and how Document Stores (like MongoDB) fix it. The Decision Matrix: We provide a clear, bias-free rule of thumb for choosing the right tool. Key Takeaways: Use SQL when relationships and consistency are critical (e.g., Financial Systems, User Billing). Use NoSQL when you need ultra-high write speeds or flexible schemas (e.g., Gaming Logs, IoT feeds, Social Content). References: Designing Data-Intensive Applications by Martin Kleppmann (Chapter 2) System Design Interview by Alex Xu #SystemDesign #SQL #NoSQL #DatabaseEngineering #SoftwareArchitecture #Scalability #ACID #MongoDB #PostgreSQL #DataModeling #BackendDevelopment