Data Engineering with AWS

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.

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), he specializes in guiding enterprise clients through complex cloud transformations with a focus on data science, AI/ML, IoT, and geospatial technologies. His career spans pivotal roles including chief data scientist and CXO at Quantela, where he led the company's AI transformation and secured Series A funding, and leadership positions at major technology companies including Symantec, Yahoo, and PubMatic.

At AWS, Sanjiv has architected several landmark projects, including DigiYatra, India's digital travel credential initiative, processing over 1 billion records, demonstrating his expertise in designing massive-scale, mission- critical architectures. He built AWS's geospatial vertical from the ground up, achieving multi-million dollar annual revenue, and has guided numerous Fortune 500 companies in oil and gas, energy, and smart cities sectors through their data transformation journeys. A recognized thought leader, Sanjiv was awarded Top Chief Architect in India 2015 and led Quantela to be recognized as a WEF Technology Pioneer. His unique combination of hands-on technical expertise, strategic vision, and deep understanding of AWS services, from real-time streaming to machine learning at scale, makes him uniquely qualified to guide readers through the complexities of building modern, enterprise-grade data platforms on AWS.