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Data engineering and AWS form the backbone of modern enterprise data architecture, enabling organizations to harness the exponential growth of data for competitive advantage. As businesses generate petabytes of information daily, the ability to build scalable, secure, and cost-effective data platforms has become critical for survival in today's data-driven economy.

This comprehensive guide takes you through the complete journey of building enterprise-grade data platforms on AWS. You will understand data lake foundations with S3, implement real-time streaming with Kinesis, and optimize batch processing using Glue. The book covers advanced topics, including data warehouse engineering with Redshift, modern architectural patterns like data mesh, and cross-boundary data sharing strategies. The guide explores the GenAI revolution transforming data platforms from human-centric to AI-native systems, covering enhanced medallion architectures that serve both traditional analytics and generative AI workloads.

By the end of this book, you will be able to design and build scalable, secure, and cost-effective data platforms on AWS. You will master the skills to process massive datasets, implement enterprise-grade security, and architect solutions for real-time analytics and ML workflows, ultimately driving significant business value.

What you will learn

● Build petabyte-scale data lakes using S3 and Lake Formation.

● Implement real-time streaming pipelines with Kinesis and Lambda.

● Design cost-optimized data warehouses using Amazon Redshift.

● Create modern data mesh architectures on AWS.

● Master DataOps practices with CI/CD and IaC.

● Architect GenAI-native platforms with enhanced medallion architectures.

● Integrate ML pipelines using SageMaker and Glue.

● Implement enterprise security and governance strategies.

Who this book is for

This book is ideal for data engineers, cloud architects, DevOps engineers, and solutions architects building data platforms on AWS. Data scientists, ML engineers, and technical managers seeking to understand modern data infrastructure implementation will also find immense value.

Table of Contents

1. Modern Data Engineering Landscape

2. Building Data Lake Foundations

3. Data Formats and Storage Optimization

4. Real-time Data Ingestion and Streaming

5. Batch Data Processing

6. Data Transformation and Quality

7. Data Warehouse Engineering with Redshift

8. Modern Data Architecture Patterns

9. Data Governance and Security

10. Cross-boundary Data Sharing and Collaborations

11. Analytics and Visualization

12. Machine Learning Integration

13. DataOps and Automation

14. GenAI Revolution in Data Engineering

15. Future-Proofing Data Platforms

Appendix: Performance Tuning Guide

Sanjiv Kumar Jha is a distinguished technology leader and data science expert with over 25 years of experience architecting and implementing large-scale data solutions. Currently serving as principal solution architect at Amazon Web Services (AWS)