📊 Data Engineering Projects

📌 Overview

This repository contains beginner-level Data Engineering projects and practice work. The goal of this repository is to learn how data is collected, processed, transformed, and stored for analysis.

It covers basic concepts like:

• Data ingestion
• Data cleaning
• ETL (Extract, Transform, Load)
• SQL queries
• Data pipeline basics

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🎯 Objectives

• Understand Data Engineering fundamentals
• Build simple ETL pipelines
• Practice SQL and Python for data processing
• Learn how data flows from source to storage

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🛠 Tools & Technologies

• Programming Language: Python

• Database: MySQL / PostgreSQL

• Libraries:

  • Pandas
  • NumPy

• Other Tools:

  • Apache Airflow (basic level)
  • Git & GitHub

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📂 Project Structure

Data-Engineering/
│
├── datasets/
├── python-scripts/
├── sql-queries/
├── airflow-dags/
└── README.md

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🚀 What You Will Find Here

• Simple data cleaning scripts
• Basic SQL practice queries
• Mini ETL projects
• Sample datasets
• Learning notes

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📘 Topics Covered

• What is Data Engineering?
• Difference between ETL and ELT
• Batch vs Real-time processing (basic understanding)
• Data warehouse basics
• Simple pipeline creation

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🔮 Future Improvements

• Add more real-world projects
• Include cloud-based examples
• Improve automation
• Add documentation for each project

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👨‍💻 Author

Govind Sharma Learning Data Engineering