Engineer Atlas
OverviewLearnInternalsModeling LabPlaygroundDatabase FinderRoadmapPracticeInterview
OverviewLearnInternalsModeling LabPlaygroundDatabase FinderRoadmapPracticeInterviewCheat SheetCompare
Database Engineering
  • Database Fundamentals
  • SQL
  • Relational Modeling
  • Normalization & Denormalization
  • Indexes
  • Query Execution & Optimization
  • Transactions
  • Concurrency & Isolation
  • PostgreSQL
  • Redis
  • NoSQL & Data Models
  • Vector Databases & Retrieval
  • Scaling
  • Distributed Databases
  • Caching
Database Internals
  • Overview
  • Build AtlasDB
  • Storage, Records & Pages
  • Index Internals
  • Buffer Management
  • WAL & Recovery
  • Transactions & MVCC Internals
  • LSM Trees
  • Query Engine
  • PostgreSQL & InnoDB Internals
  • Distributed Internals
  • Performance Internals
Database/Learn/NoSQL & Data Models
Database Engineering

NoSQL & Data Models

Document, key-value, wide-column, graph, search, time-series, vector: what each model is actually good at.

See how this works internally:LSM Trees →
SQL vs NoSQL: Choosing a Data Model

Relational, document, key-value, wide-column, graph, search, time-series and vector are not a ladder from old to scalable; each is a different bet on which access pattern you will need most, and the price is the patterns you give up.

Document Databases: Embed or Reference
▶ interactive

A document store trades joins for locality — the whole object arrives in one read — and the design decision that replaces normalization is whether each relationship is embedded in the parent or referenced by id.

Wide-Column, Graph, Search and Time-Series

Four specialised models, each built around one query shape: partition-local time-ordered reads at huge write rates, multi-hop traversal, relevance-ranked text, and range aggregates over time.

Engineer Atlas
GitHub·LinkedIn