System Design for Data Engineers: Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews (Data Engineering) - Softcover

Inhaltsangabe

Master Data Engineering System Design — From Fundamentals to Real-World Production Systems

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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