🚀 Data Engineering System Design

A comprehensive collection of Data Engineering System Design concepts, architectures, best practices, and interview notes for modern cloud-based data platforms.

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📖 About This Repository

This repository serves as a practical knowledge base for designing scalable, secure, and reliable data engineering systems.

It covers real-world architectures used by modern organizations for building batch processing, streaming platforms, cloud data lakes, data warehouses, ETL pipelines, monitoring, security, and performance optimization.

The goal is to explain how production-grade data platforms are designed, rather than focusing only on implementation.

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📚 Repository Structure

data-engineering-system-design/

├── 01-Data-Lake-Architecture
├── 02-Data-Warehouse-Design
├── 03-Batch-ETL-Pipeline
├── 04-Real-Time-Streaming
├── 05-CDC-Pipeline
├── 06-Orchestration
├── 07-Data-Modeling
├── 08-Performance-Optimization
├── 09-Data-Quality
├── 10-Monitoring
├── 11-Security
├── 12-Cost-Optimization
├── 13-System-Design-Interview

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📌 Topics Covered

Data Lake

• Data Lake Architecture
• Medallion Architecture
• Bronze / Silver / Gold
• Batch Ingestion
• Streaming Ingestion
• Partitioning
• File Formats

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Data Warehouse

• Star Schema
• Snowflake Schema
• Slowly Changing Dimensions
• Fact Tables
• Dimension Tables
• Data Marts

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ETL & ELT

• Batch Processing
• Incremental Loads
• Full Loads
• Scheduling
• Error Handling
• Retry Strategy

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Real-Time Streaming

• Kafka
• Event-Driven Architecture
• Spark Streaming
• Streaming ETL