System Design for Data Engineers : Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews
Language: English
Published by Independently Published Jun 2026, 2026
Series: Book 6 of 15 - Data Engineering
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 49.15
Quantity: 2 available
Add to basketItem description from seller
Neuware - Master Data Engineering System Design - From Fundamentals to Real-World Production SystemsSystem 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 MeshWho 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 engineeringEvery 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.…
Seller Inventory # 9798183934342
- Title
- System Design for Data Engineers : Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews
- Author
- Masud Mondal
- Publisher
- Independently Published Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798183934342
- Item weight
- 418 grams
- Dimensions
- 229x152x17 mm
- Series
- Book 6 of 15: Data Engineering
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
"Synopsis" may belong to another edition of this title.
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