Ultimate Data Engineering Design Patterns

Build data pipelines that perform, scale, and last in production.

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com
● Comprehensive data engineering pattern coverage spanning ingestion, storage, transformation, batch processing, and stream processing.
● Hands-on implementation using Apache Spark, Kafka, Airflow, and cloud-native tools across real-world industry case studies.
● Production-grade pipeline engineering with DataOps, governance, observability, and scalability strategies for modern data platforms.

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.

The final section covers data quality, governance, compliance, scalability optimization, and DataOps practices with end-to-end pipeline implementation and future trends. Thus, by the end of the book, you can design, build, and operate production-grade data pipelines with confidence, applying proven patterns to solve real-world data challenges at scale.

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.

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

About the Authors
Bragadeesh Sundararajan is an AI and data science visionary leader with more than 16 years of experience and expertise, driving business impact through intelligent solutions. Recognized among India’s Top 100 AI Leaders, he champions ethical, accessible AI, blending technical expertise and strategy to empower organizations and future data professionals.

About the Technical Reviewer
Abhishek Singh is an AI and data professional with a data engineering focus, whose work has spanned US healthcare, the UK public sector, and financial services, including banking and insurance. He has led data engineering teams and delivered large-scale data platforms - consistently working at the intersection of scalable data infrastructure and real-world business outcomes.

At NatWest Group, one of the United Kingdom's largest banking groups, Abhishek led a cross-geography engineering team delivering data-driven applications. He now works at EY, UK, in Financial Services Consulting, where he develops AI and data assets as well as explores AI-augmented approaches to data engineering and DataOps.

Increasingly focused on applied AI, Abhishek actively works with LLMs and agentic systems, bridging the gap between data engineering and intelligent, AI-driven solutions. Beyond his technological consulting work, Abhishek is an Applied AI Researcher at Erdos Research and hosts community meetups for AI and data technology enthusiasts in Scotland.

Anusha Dasarakothapalli is a Principal Software Engineer at Amazon, in the Applied AI services organization, specializing in distributed systems, large-scale data and streaming platforms, and AI-driven software infrastructure. She has over 10 years of experience designing and operating mission-critical cloud services and developer platforms used at a global scale.

Anusha’s work focuses on building reliable, high-performance systems for data streaming, storage, and intelligent software development workflows. Throughout her career, Anusha has contributed to systems spanning real-time streaming platforms, large-scale storage services such as Amazon S3, Amazon S3 Glacier,

Amazon Managed Streaming for Apache Kafka (MSK) and observability frameworks for modern AI-enabled software development. Her work currently focuses on developing next-generation agentic solutions for software development teams powered by Artificial Intelligence (AI).

In addition to her engineering work, Anusha actively contributes to the broader technology community by participating in technical reviews, evaluating innovative research as well as engineering work, and supporting the advancement of emerging technologies in distributed systems and AI-driven software development.

Vidya Sambasivam has over 28 years of experience in technology, software products, and strategic planning, with a specialized focus on engineering enterprise applications for the financial sector. Throughout her career, she has led the rollout of complex projects and products for global banks, insurance companies, and large NBFCs, including GE Money, Bajaj Finserv, BMW Financial Services, Fullerton, and HDFC Life. She has a proven track record of identifying and nurturing technical talent, having led high-performance delivery teams during her tenure at Ebixcash

Financial Technologies (Indus Software) and Integra.

Vidya currently serves as the Chief Information Officer at Dvara KGFS, where she is driving value by establishing a scalable, secure, and resilient digital ecosystem. She remains a "hands-on techie" at heart, focusing on the practical applications of Digital Transformation, Cloud native architecture, and AI/ML. She is passionate about leveraging data-driven insights and emerging technologies to solve intricate business challenges and mentor the next generation of technology leaders.