Intermediate Backend

Databases (SQL vs NoSQL)

PostgreSQL, MongoDB, data modeling, and query optimization.

Storing and Retrieving Data

Databases are the backbone of most applications, providing persistent storage and efficient data retrieval.

SQL (Relational)

Structure: Tables with rows and columns
Schema: Defined upfront, enforced by database
Relationships: Foreign keys, JOINs
ACID: Strong consistency guarantees
Examples: PostgreSQL, MySQL, SQLite

-- SQL Example
SELECT users.name, COUNT(posts.id) as post_count
FROM users
LEFT JOIN posts ON users.id = posts.user_id
GROUP BY users.id
ORDER BY post_count DESC;

NoSQL (Non-Relational)

Document: MongoDB, CouchDB (JSON-like docs)
Key-Value: Redis, DynamoDB
Wide-Column: Cassandra, ScyllaDB
Graph: Neo4j, ArangoDB

When to Use What

SQL: Complex queries, transactions, data integrity
NoSQL: Flexible schema, horizontal scaling, specific access patterns

ORMs and Query Builders

Prisma: Type-safe ORM with migrations
Drizzle: TypeScript SQL query builder
Knex: SQL query builder
Mongoose: MongoDB ODM

Additional Resources