EBOOK
Ultimate Data Engineering Design Patterns
Design and Build Scalable Data Pipelines Using Proven Patterns for Modern Data Platforms (English Ed
Bragadeesh Sundararajan(0)
About
Build data pipelines that perform, scale, and last in production.
Book Description
Data engineering is the backbone of every modern data-driven organization - and the ability to design scalable, reliable pipelines is the most in-demand skill across analytics, AI, and platform engineering. Ultimate Data Engineering Design Patterns provides a comprehensive, pattern-driven guide to building robust data infrastructure, from foundational ingestion and storage to stream processing, governance, and cloud-native deployment.
You begin with core architectural patterns and data engineering fundamentals, then progressively work through ingestion, storage, batch processing, stream processing, and transformation patterns using tools such as Apache Spark, Kafka, and Airflow. Each chapter grounds concepts in hands-on exercises and industry case studies drawn from finance, healthcare, and e-commerce, ensuring every pattern is immediately applicable to real engineering scenarios.
What you will learn
? Design scalable batch and real-time data pipelines using proven engineering patterns.
? Implement reliable data ingestion workflows across diverse sources and formats.
? Build efficient data lakes, warehouses, and lakehouse architectures for modern platforms.
? Apply data governance, quality, and observability practices to production pipelines.
? Optimize pipeline performance and scalability using cloud-native tools and strategies.
? Implement DataOps practices for operationalising and maintaining enterprise data platforms.
Table of Contents
1. Introduction to Data Engineering
2. Data Engineering Fundamentals
3. Architectural Patterns in Data Engineering
4. Data Ingestion Patterns in Data Engineering
5. Storage Design Patterns in Data Engineering
6. Batch Processing Patterns
7. Stream Processing Patterns
8. Data Transformation and Enrichment Patterns
9. Machine Learning Engineering Patterns
10. Data Quality Patterns
11. Data Governance and Compliance
12. Scalability and Performance Optimization
13. Building End-to-End Data Pipelines
14. Operationalizing Data Pipelines
15. Future of Data Engineering
Index
Book Description
Data engineering is the backbone of every modern data-driven organization - and the ability to design scalable, reliable pipelines is the most in-demand skill across analytics, AI, and platform engineering. Ultimate Data Engineering Design Patterns provides a comprehensive, pattern-driven guide to building robust data infrastructure, from foundational ingestion and storage to stream processing, governance, and cloud-native deployment.
You begin with core architectural patterns and data engineering fundamentals, then progressively work through ingestion, storage, batch processing, stream processing, and transformation patterns using tools such as Apache Spark, Kafka, and Airflow. Each chapter grounds concepts in hands-on exercises and industry case studies drawn from finance, healthcare, and e-commerce, ensuring every pattern is immediately applicable to real engineering scenarios.
What you will learn
? Design scalable batch and real-time data pipelines using proven engineering patterns.
? Implement reliable data ingestion workflows across diverse sources and formats.
? Build efficient data lakes, warehouses, and lakehouse architectures for modern platforms.
? Apply data governance, quality, and observability practices to production pipelines.
? Optimize pipeline performance and scalability using cloud-native tools and strategies.
? Implement DataOps practices for operationalising and maintaining enterprise data platforms.
Table of Contents
1. Introduction to Data Engineering
2. Data Engineering Fundamentals
3. Architectural Patterns in Data Engineering
4. Data Ingestion Patterns in Data Engineering
5. Storage Design Patterns in Data Engineering
6. Batch Processing Patterns
7. Stream Processing Patterns
8. Data Transformation and Enrichment Patterns
9. Machine Learning Engineering Patterns
10. Data Quality Patterns
11. Data Governance and Compliance
12. Scalability and Performance Optimization
13. Building End-to-End Data Pipelines
14. Operationalizing Data Pipelines
15. Future of Data Engineering
Index