Backend Technology Comparisons

Honest, experience-based comparisons of backend frameworks, databases, tools, and technologies. No affiliate bias, just real engineering perspective.

9 Comparisons
// Python Frameworks

FastAPI vs Django

An honest, experience-based comparison of Python's two most popular web frameworks. FastAPI excels at modern APIs; Django shines for full-stack applications.

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// Python Frameworks

FastAPI vs Flask

FastAPI and Flask are both lightweight Python frameworks, but they take fundamentally different approaches. FastAPI is built for modern async APIs; Flask is a minimalist foundation you build upon.

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// API Architecture

REST vs GraphQL

REST and GraphQL are the two dominant approaches to API design. REST uses multiple endpoints with fixed data structures; GraphQL uses a single endpoint with flexible queries.

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// Databases

PostgreSQL vs MySQL

PostgreSQL and MySQL are the two most popular open-source relational databases. PostgreSQL leads in features and data integrity; MySQL in simplicity and read performance.

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// DevOps

Docker vs Kubernetes

Docker creates and runs containers. Kubernetes orchestrates them at scale. They're complementary tools, not competitors - understanding when you need each is key.

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// Databases

Redis vs Memcached

Both Redis and Memcached are in-memory key-value stores used for caching. Redis offers richer data structures and features; Memcached is simpler and uses less memory per key.

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// Python Libraries

SQLAlchemy vs Django ORM

SQLAlchemy offers maximum flexibility with both ORM and SQL expression layers. Django ORM trades some power for simplicity and tight Django integration. The right choice depends on your framework and query complexity.

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// Python Architecture

ASGI vs WSGI

WSGI served Python web development for 20 years. ASGI is its async successor, enabling WebSockets, long-lived connections, and concurrent I/O handling.

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// Python Libraries

Pydantic vs Marshmallow

Pydantic uses Python type hints for validation with automatic coercion. Marshmallow uses explicit field definitions with a schema-based approach. Both validate data, but their philosophies differ significantly.

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