Data at Rest, Data in Motion: Databases
Schema design, query optimization, and data modeling for relational and NoSQL systems at scale. PostgreSQL, Redis, and MongoDB.
Start reading →01Chapter 1: Why Database Choices Matter02Chapter 2: Relational Fundamentals: Tables, Keys, Constraints, Normalization03Chapter 3: ACID Properties and Transactions04Chapter 4: Isolation Levels and the Anomalies Each One Prevents05Chapter 5: SQL Essentials and Query Optimization06Chapter 6: Indexing Strategies: B-Tree, Hash, Composite, Covering07Chapter 7: NoSQL Categories: Document, Key-Value, Wide-Column, Graph08Chapter 8: Schema Design and Data Modeling (Relational and NoSQL Access Patterns)09Chapter 9: The CAP Theorem and Consistency Models10Chapter 10: Replication: Leader-Follower and Multi-Leader11Chapter 11: Sharding and Partitioning Strategies12Chapter 12: Caching Layers and Invalidation Strategies13Chapter 13: Evolving Schemas Safely in Production14Chapter 14: Diagnosing and Fixing a Slow Query, End to End15Chapter 15: Schema Design Interview Exercises16Chapter 16: Database Concepts in System Design and Production Debugging
Other tracks
Python DSA — CoursePython DSA — Interview PrepPython DSA — BeginnerPython Syntax & DSA ReferenceGit MasteryCognitive Skills & Mental ToolsCode Optimization RulesObjects First: OOP FundamentalsPatterns of Good DesignUnder the Hood: Operating SystemsHow the Bytes Travel: NetworkingSharing Time: Concurrency & MultithreadingClean by Default: Coding StandardsThe Review: Code ReviewFrom Idea to Production: the SDLCDesigning at Scale: System DesignWhiteboard to Blueprint: HLD & LLDBuilt to Last: Reliability & ScalingTell Me About a Time: Behavioral Interviews