DDIA Chapter 1 Explained: Cloud vs Self-Hosted | Designing Data-Intensive Applications

Chapter 1 of DDIA, part 2 - and we're finishing what we started. First, a quick patch from last video: Systems of Record vs Derived Data Systems - the idea that data has a birthplace, and everything else is a child of that source. Then the main event: Cloud vs Self-Hosted. Not a lecture, just what I understood from the book. What we cover: ▸ The core rule of thumb: core competency vs commodity ▸ The build vs buy spectrum: from full in-house to fully outsourced ▸ When cloud wins and when self-hosted wins ▸ Pros and cons of each: including vendor lock-in ▸ What "cloud native" actually means ▸ Virtual disk vs object storage ▸ The abstraction ladder: when to use a high-level service vs build lower This playlist exists because I failed an interview question, they asked me to design a RAG system for millions of documents and I had no idea how. By the end of this series, I will. Next video: Single node vs Distributed Systems & Data Protection Regulation 📚 Book: Designing Data-Intensive Applications by Martin Kleppmann 🔗 Other Playlists: ML from Scratch: • Linear Regression from Scratch: Math + Cod... Nand2Tetris: • nand2tetris Solutions - Intro 📌 GitHub: https://github.com/amithkumars-ds #DDIA #DesigningDataIntensiveApplications #DataEngineering #SystemDesign #SoftwareEngineering #MLEngineer #DataScience