System Design for Data Engineers
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Home > Computing and Information Technology Books > Databases > Data warehousing > System Design for Data Engineers: Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews(Data Engineering)
System Design for Data Engineers: Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews(Data Engineering)

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


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About the Book

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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Product Details
  • ISBN-13: 9798183934342
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 229 mm
  • No of Pages: 310
  • Returnable: N
  • Spine Width: 17 mm
  • Weight: 467 gr
  • ISBN-10: 818393434X
  • Publisher Date: 23 Jun 2026
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Series Title: Data Engineering
  • Sub Title: Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews
  • Width: 152 mm


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