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Fundamentals 🧱 (Core Data Engineering Concepts)
This section builds your foundation for everything in Data Engineering.
Before learning PySpark, Spark Internals, or System Design, you must understand:
🧠 How data systems actually work at a fundamental level.
🎯 What You Will Learn
This module covers the core building blocks of data engineering:
- How data is modeled
- How storage systems work
- How data is processed
- How pipelines are designed
- How warehouses store data
- Basic system design concepts
🧭 Learning Path Inside Fundamentals
Follow this order:
1. Data Modeling
Understand how data is structured in systems.
👉 /fundamentals/01-data-modeling
2. Storage Systems
Learn how data is stored and retrieved.
👉 /fundamentals/02-storage
3. Processing Models
Batch vs stream vs hybrid processing.
👉 /fundamentals/03-processing
4. Data Pipelines Basics
How data moves across systems.
👉 /fundamentals/04-data-pipeline
5. Data Warehousing
How analytical systems are built.
👉 /fundamentals/05-data-warehouse
6. System Design Basics
Intro to scalable architecture thinking.
👉 /fundamentals/06-system-design
🔥 Why This Section Matters
Without fundamentals:
- PySpark feels like random APIs
- Spark Internals feels confusing
- System Design becomes memorization instead of reasoning
📌 Goal
By the end of this section, you should be able to:
- Understand how data flows in systems
- Understand storage vs processing tradeoffs
- Think in terms of system components
- Prepare for real interview discussions
“Strong systems are built on strong fundamentals — everything else is just abstraction.”