This repository documents my daily practice of analyzing real-world data engineering problems with a production-oriented mindset.
To strengthen my data engineering thinking by focusing on:
- system design trade-offs
- data reliability
- incremental and scalable data processing
Rather than tool-heavy tutorials, this repository emphasizes why certain decisions are made in production data systems.
- Daily Cases: Realistic data engineering scenarios with analysis and minimal working solutions
- Applied Projects: How these concepts are applied to my own projects
- Knowledge Base: Structured notes on core data engineering concepts
Junior / Early Mid Data Engineer
- Incremental & CDC-style pipelines
- Idempotent data processing
- Data quality & failure handling
- Distributed systems fundamentals
Each case includes:
- problem definition
- engineering analysis
- trade-offs
- minimal SQL / Python examples
- real-world application notes