Data Pipeline ETL Framework

A lightweight, modular ETL (Extract → Transform → Load) framework built in Python to demonstrate clean data engineering patterns.
This project simulates a real-world ML/analytics workflow by ingesting raw data, applying transformations, validating schema and quality rules, and preparing the data for downstream machine learning pipelines.

It is intentionally simple, production-inspired, and designed to showcase strong backend engineering + Python data processing skills.

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✨ Features

• Modular ETL stages → ingestion, transformation, validation
• Reusable pipeline architecture using clean function boundaries
• Pydantic models for strict input validation
• Extensible design → plug in new sources, transformations, and sinks
• ML-ready output for downstream model training or batch jobs
• Clear folder structure used in real data engineering teams

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📂 Project Structure

data-pipeline-etl-framework/
│
├── README.md                # Project documentation
├── requirements.txt         # Dependencies
│
└── etl_pipeline/
    ├── __init__.py
    ├── main.py              # Pipeline entrypoint
    ├── ingestion.py         # Raw data ingestion
    ├── transform.py         # Transform logic
    ├── validation.py        # Data validation rules
    └── models.py            # Pydantic schemas

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🚀 How to Run

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/data-pipeline-etl-framework.git
cd data-pipeline-etl-framework

2. Install dependencies

pip install -r requirements.txt

3. Run the ETL pipeline

python -m etl_pipeline.main

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🧩 Example Output

[ INGEST ] Loaded 50 records.
[ TRANSFORM ] Cleaned & normalized data.
[ VALIDATION ] Schema validation passed.
[ DONE ] Final dataset ready for ML workflows.

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🔧 Extending the Pipeline

You can easily add more features:

➤ New ingestion sources

• CSV / JSON files
• Databases (PostgreSQL, MySQL)
• Cloud storage (AWS S3, GCS)

➤ Additional transformations

• Feature engineering for ML models
• Normalizing + scaling numeric features
• Text cleaning for NLP pipelines

➤ Production upgrades

• Airflow / Prefect orchestration
• Kafka-based streaming ingestion
• Data quality monitoring with Great Expectations

The current architecture allows extensions without modifying existing logic.

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🛠 Tech Stack

• Python 3.10+
• Pydantic
• Standard Python ETL patterns
• Modular, production-inspired design

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📌 Use Cases

• Showcasing backend + data engineering skills
• ML feature preprocessing pipelines
• ETL teaching project
• Prototype for analytics workflows

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🏁 Summary

This project demonstrates your ability to design real data-processing systems that are modular, validated, and ML-ready — exactly what modern AI/ML backend engineering roles require.