When a relational database is the wrong tool, and which non-relational store to reach for instead. Why NoSQL exists and the four families, then hands-on with each — key-value, document, wide-column, and graph — modeling data around access patterns, and choosing between them (and combining them) with polyglot persistence.
Before you start
Knowing relational databases first makes the trade-offs land — the PostgreSQL course is the ideal companion, and the Caching course goes deeper on Redis as a cache.
Why NoSQL & the Four Types
NoSQL is not "better than SQL" — it makes a different trade. Learn what relational databases give up at scale, the BASE vs ACID trade-off, and the four NoSQL families and when each fits.
Key-Value Stores
The simplest NoSQL model: a giant distributed hash table. Learn key-value operations with Redis and DynamoDB, TTLs, and the access patterns — caching, sessions, counters — they excel at.
Document Databases
Document stores hold flexible, nested JSON-like records and let you query any field. Learn MongoDB — documents and collections, querying, indexes, and the embed-vs-reference modeling decision.
Wide-Column Stores
Wide-column stores like Cassandra handle enormous write throughput by partitioning data across a cluster. Learn the partition-key/clustering-key model and why you design one table per query.
Graph Databases
When relationships are the point — social networks, fraud rings, recommendations — a graph database models nodes and edges as first-class citizens. Learn Neo4j and querying with Cypher.
Choosing & Polyglot Persistence
There is no single best database. Learn a decision framework for picking the right store per workload, and how real systems combine several — polyglot persistence — behind one application.