Data Engineering Study Material

# Data Engineering Study Material

> Skill by [ara.so](https://ara.so) — Data Skills collection.

## Overview

This project is a comprehensive study guide and reference repository for data engineering concepts, tools, and practices. It serves as a centralized resource for learning core data engineering principles, understanding modern data stack components, and preparing for data engineering roles.

The repository covers:
- Data engineering fundamentals and architecture patterns
- ETL/ELT pipeline design and implementation
- Data warehousing and lake architectures
- Streaming and batch processing frameworks
- Cloud data platforms (AWS, GCP, Azure)
- Data quality, governance, and observability
- Infrastructure as Code and orchestration tools
- Interview preparation and best practices

## Installation

This is a study material repository, not an installable package. Clone it to access the materials:

```bash
git clone https://github.com/Ahmeduddin3403/data-engineering-study-material.git
cd data-engineering-study-material
```

## Repository Structure

The materials are typically organized by topic area:

```
data-engineering-study-material/
├── fundamentals/          # Core concepts and principles
├── tools/                 # Tool-specific guides
├── architectures/         # Design patterns and architectures
├── pipelines/             # ETL/ELT examples
├── cloud-platforms/       # Cloud-specific implementations
├── streaming/             # Real-time processing
├── batch-processing/      # Batch job patterns
├── data-quality/          # Testing and validation
├── orchestration/         # Workflow management
├── interview-prep/        # Interview questions and answers
└── projects/              # Hands-on project examples
```

## Core Data Engineering Concepts

### ETL Pipeline Example (Python)

```python
import pandas as pd
from sqlalchemy import create_engine
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ETLPipeline:
    """Simple ETL pipeline for extracting, transforming, and loading data"""
    
    def __init__(self, source_path, target_conn_string):
        self.source_path = source_path
        self.engine = create_engine(target_conn_string)
    
    def extract(self):
        """Extract data from source"""
        logger.info(f"Extracting data from {self.source_path}")
        df = pd.read_csv(self.source_path)
        logger.info(f"Extracted {len(df)} rows")
        return df
    
    def transform(self, df):
        """Transform data: clean, deduplicate, enrich"""
        logger.info("Transforming data")
        
        # Remove duplicates
        df = df.drop_duplicates()
        
        # Handle missing values
        df = df.fillna({
            'numeric_column': 0,
            'string_column': 'Unknown'
        })
        
        # Add derived columns
        df['created_date'] = pd.to_datetime(df['timestamp']).dt.date
        
        # Data validation
        df = df[df['amount'] > 0]
        
        logger.info(f"Transformed to {len(df)} rows")
        return df
    
    def load(self, df, table_name):
        """Load data to target database"""
        logger.info(f"Loading data to {table_name}")
        df.to_sql(table_name, self.engine, if_exists='append', index=False)
        logger.info("Load complete")
    
    def run(self, table_name):
        """Execute full ETL pipeline"""
        try:
            df = self.extract()