Modular Architecture, Virtual Environments & Automated Pytest

[!NOTE] Writing production Python for data engineering demands moving beyond unstructured notebook scripts to reproducible src/ layout packages managed by modern dependency resolvers (uv, poetry, or pip-tools) with parameterized unit tests.

1. Enterprise Repo Layout

``` my-data-pipeline/ ├── pyproject.toml ├── src/ │ └── data_pipeline/ │ ├── init.py │ ├── extractors/ │ ├── transformers/ │ └── loaders/ └── tests/ ├── conftest.py ├── unit/ └── integration/ ```

Modern Python Packaging, uv / pyproject.toml & Testing Strategy

• Reproducible Dependency Locking: Modern data repos standardize on pyproject.toml + lockfiles (uv.lock or poetry.lock) instead of unpinned pip install pandas, preventing surprise breaking minor releases inside Docker CI images.
• Testing Hierarchy for Data Pipelines:
  1. Unit Tests (pytest + fixtures): Test pure transformation logic, edge NULLs, empty inputs, and SCD boundaries in <1s without touching cloud S3/Snowflake!
  2. Contract / Schema Tests: Validate Pydantic/Pandera schemas against golden sample fixtures.
  3. Ephemeral Integration Tests: Spin up local DuckDB / SparkSession.master("local[2]") in CI before merging to main.