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System Design for Data Engineers: Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews
Paperback
Series: Data Engineering, Book 6
Publisher: Independently Published
Published: Jun 23 2026
Pages: 310
Weight: 0.92
Height: 0.65 Width: 6.00 Depth: 9.00
Language: English
System design interviews trip up data engineers who are strong on execution but have never been shown how to structure a complete architectural answer. This book fixes that gap - and gives you the production knowledge to back it up.
What you will learn:
- How to approach any system design question using a six-step framework that works every time
- The fundamentals of distributed systems: CAP theorem, replication, partitioning, consistency models, and message delivery guarantees
- How to design batch pipelines, streaming pipelines, and CDC architectures from scratch
- Modern data architectures: data warehouse (Kimball, Inmon, Medallion), data lake (Bronze/Silver/Gold), and lakehouse (Delta Lake, Iceberg, Hudi)
- AWS, Azure, and GCP data services - and how to combine them into production-ready platforms
- Five complete real-world case studies: Uber GPS platform, Netflix analytics, e-commerce data platform, real-time fraud detection, and an AI/ML platform with feature store and RAG
- 20 most-asked system design interview questions with full answers, architectures, and common mistakes
- Where data engineering is heading: AI-assisted pipelines, the real-time lakehouse, vector databases, and Data Mesh
Who this book is for:
- Junior to mid-level data engineers preparing for system design interviews
- Data engineering beginners who want to understand how components fit together into real systems
- College students and freshers entering the data engineering field
- Professionals moving from analytics or software engineering into data engineering
Every chapter follows a consistent structure: core concepts, real-world examples, architecture diagrams, common mistakes, and interview questions. The writing is practitioner-level - no academic jargon, short paragraphs, and honest trade-off discussions throughout.
This is a standalone book. No prior system design experience required - only a basic familiarity with SQL and Python.
Topics covered: system design fundamentals - scalability - distributed systems - OLTP vs OLAP - data modeling - star schema - SCD Type 2 - storage formats - Parquet - Avro - Delta Lake - Apache Iceberg - batch pipelines - Airflow - streaming pipelines - Apache Kafka - Flink - CDC - Debezium - data warehouse - data lake - lakehouse - AWS - Azure - GCP - Redshift - Snowflake - BigQuery - feature store - RAG - vector databases - fraud detection - A/B testing - interview framework
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