Automation & Data Engineering Prototypes

This repository contains prototypes for ETL Pipelines, Workflow Automation, Data Modeling, and API Development.

Blueprint Overview

Based on Apache Airflow docs (airflow.apache.org) and FastAPI (fastapi.tiangolo.com), we'll automate data workflows and APIs.

Prototypes

1. ETL Pipelines: Extract, transform, load data.
2. Workflow Automation: Orchestrate tasks with schedulers.
3. Data Modeling: Schema design and validation.
4. API Development: RESTful endpoints for data access.

Tech Stack

• Python 3.8+
• Libraries: Pandas, SQLAlchemy, FastAPI, Airflow.
• Dependencies: Install via pip install pandas sqlalchemy fastapi airflow.

Key Components

• ETL Pipeline: import pandas as pd; df = pd.read_csv('data.csv')
• API Endpoint: from fastapi import FastAPI; app = FastAPI()

Implementation Notes

• Files Added: etl_pipeline.py (data processing), workflow_automation.py (Airflow DAG), data_modeling.py (SQLAlchemy schemas), api_development.py (FastAPI endpoints), test_etl.py (unit tests).
• How to Run: Install deps, run python api_development.py for API. Tests: python test_etl.py.
• Trade-offs: SQLite for simplicity; scale to PostgreSQL for production.