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System Design for Data Engineers: Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews (Data Engineering, Band 6) - Softcover
Buch 6 von 15: Data Engineering
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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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System Design for Data Engineers: Design Scalable Data Systems, Pipelines, Lakehouses & Cloud Architectures for Real-World Projects and Interviews
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Paperback. Zustand: new. Paperback. 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 timeThe fundamentals of distributed systems: CAP theorem, replication, partitioning, consistency models, and message delivery guaranteesHow to design batch pipelines, streaming pipelines, and CDC architectures from scratchModern 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 platformsFive 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 RAG20 most-asked system design interview questions with full answers, architectures, and common mistakesWhere 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 interviewsData engineering beginners who want to understand how components fit together into real systemsCollege students and freshers entering the data engineering fieldProfessionals 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 This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798183934342
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Taschenbuch. Zustand: Neu. 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. Bestandsnummer des Verkäufers 9798183934342
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