Designing a secure expense tracker API with FastAPI and PostgreSQL — relational schema, JWT auth, monthly reports with SQL GROUP BY and query optimisation.
Users want to log expenses across categories and instantly see monthly and category-wise reports — which needs a clean relational design and fast aggregate queries as data grows.
A FastAPI service with Pydantic validation and a normalised PostgreSQL schema managed by SQLAlchemy and Alembic. Reports are built with SQL aggregates (GROUP BY, date_trunc) and supported by the right indexes.
Normalise users, categories, expenses and budgets with foreign keys and constraints.
Version every schema change with Alembic so the database can be rebuilt anywhere.
FastAPI routers with Pydantic models for request validation and clean responses.
Aggregate queries with JOIN, GROUP BY and date functions; read the query plan and add indexes.
Cover endpoints with pytest and deploy with environment-based configuration.
Every line of AI-generated code is reviewed and understood before it ships.
Certificate after clearing the final interview with 90%+.
This project is part of Backend Development — Python track.
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