🚀 Data Engineer Preparation Guide

🚀 End-to-end AI-powered Data Engineering preparation repository covering SQL, PySpark, Azure, AWS, GCP, Data Modeling, Pipelines, and real-world projects with interview-focused explanations.

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📚 Table of Contents

• What you'll learn
• Who is this for
• Quick Start
• Repository Navigation
• Folder Guide
• Contributing
• License

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🎯 What you'll learn

• SQL → Basics → Advanced → Window Functions → Case Studies
• PySpark → Transformations, DAG, Optimization, Streaming
• Data Modeling → Star/Snowflake, SCD, Fact/Dimension, Keys
• Cloud Platforms → Azure, AWS, GCP (real-world usage)
• Pipelines → Batch, Streaming, Orchestration, Error Handling
• Projects → End-to-end systems with architecture + code
• Interview Preparation → SQL, PySpark, System Design, Behavioral

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📊 Who is this for

• 🧑‍🎓 Freshers building strong foundations
• 🔄 Engineers switching to Data Engineering
• 💼 Experienced professionals targeting product companies
• 🏢 Teams onboarding data engineers

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🚀 Quick Start

git clone https://github.com/yourusername/data-engineer-prep.git
cd data-engineer-prep

Start here:

• 📘 Fundamentals
• 🧠 SQL
• ⚡ PySpark
• 🚀 Projects
• 🧪 Interview Preparation

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🧭 Repository Navigation

📌 Core Tracks

• Fundamentals
• SQL
• PySpark
• Cloud
• Data Modeling
• Pipelines
• Projects
• Interview Preparation
• Resources

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🔥 High-Value Starting Points

⚠️ Ensure these files exist to avoid 404 errors

• SQL Basics
• Window Functions
• Spark Architecture
• Pipeline Orchestration
• Data Modeling Types
• Azure Overview
• AWS Overview
• GCP Overview

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🗂️ Folder Guide

• fundamentals/ → Core data engineering concepts
• sql/ → Problem-solving + interview-focused SQL
• pyspark/ → Spark internals + real-world patterns
• cloud/ → Azure, AWS, GCP services and architectures
• data_modeling/ → Warehouse + lakehouse modeling
• pipelines/ → Batch + streaming system design
• projects/ → End-to-end real-world implementations
• interview_preparation/ → Interview questions & strategies
• resources/ → Learning materials
• datasets/ → Practice datasets

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🤖 AI-Powered Features

This repository leverages AI to:

• Generate structured learning paths
• Provide interview-focused explanations
• Build real-world project blueprints

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🤝 Contributing

1. Fork the repository
2. Create a branch (git checkout -b feature/my-update)
3. Commit changes (git commit -m "Improve topic coverage")
4. Push (git push origin feature/my-update)
5. Open a Pull Request

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📄 License

Licensed under the MIT License. See LICENSE.

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