Python Backend

Build a production-grade Python backend by using FastAPI for high-throughput asynchronous APIs and Celery for reliable background task processing. This approach handles real-time requests while asynchronously executing retriable jobs like email notifications.

Pydantic validation enforces data integrity by defining strict schema models, while SQLAlchemy 2.0 handles asynchronous database access. Together they ensure type safety from the API request layer down to the ORM data mapping operations.

The best way to optimize database access in a Django backend is implementing specific ORM query patterns. These patterns reduce query overhead and ensure efficient data retrieval for high-performance application workflows.

Yes, you can use dependency injection and middleware in FastAPI for microservices. These patterns enable modular component management and request interception, which are essential for building scalable asynchronous API architectures.

You need asynchronous database access and Celery task management for order processing when handling real-time API requests alongside background jobs. This setup processes order creation synchronously while asynchronously executing email confirmations.