Unlock the Power of AWS Data Engineering and Build Smarter Pipelines for Data-Driven Success.
Key Features
? Gain an in-depth understanding of essential AWS services such as S3, DynamoDB, Redshift, and Glue to build scalable data solutions.
? Learn to design efficient, fault-tolerant data pipelines while adhering to best practices in cost management and security.
Book Description
In today’s data-driven era, mastering AWS data engineering is key to building scalable, secure pipelines that drive innovation and decision-making. Ultimate AWS Data Engineering is your comprehensive guide to mastering the art of building robust, cost-effective, and fault-tolerant data pipelines on AWS. Designed for data professionals and enthusiasts, this book begins with foundational concepts and progressively explores advanced techniques, equipping you with the skills to tackle real-world challenges.
Throughout the chapters, you’ll dive deep into the core principles of data replication, partitioning, and load balancing, while gaining hands-on experience with AWS services like S3, DynamoDB, Redshift, and Glue. Learn to design resilient data architectures, optimize performance, and ensure seamless data transformation—all while adhering to best practices in cost-efficiency and security.
Whether you aim to streamline your organization’s data flow, enhance your cloud expertise, or future-proof your career in data engineering, this comprehensive guide offers the practical knowledge and insights you need to succeed. By the end, you will be ready to craft impactful, data-driven solutions on AWS with confidence and expertise.
What you will learn
? Design scalable data pipelines using core AWS data engineering tools.
? Master data replication, partitioning, and sharding techniques on AWS.
? Build fault-tolerant architectures with AWS scalability and reliability.
Table of Contents
1. Unveiling the Secrets of Data Engineering
2. Architecting for Scalability: Data Replication Techniques
3. Partitioning and Sharding: Optimizing Data Management
4. Ensuring Consistency: Consensus Mechanisms and Models
5. Balancing the Load: Achieving Performance and Efficiency
6. Building Fault-Tolerant Architectures
7. Exploring the Realm of AWS Data Storage Services
8. Orchestrating Data Flow
9. Advanced Data Pipelines and Transformation
10. Data Warehousing Demystified
11. Visualizing the Unseen
12. AWS Machine Learning: Classic AI to Generative AI
13. Advanced Data Engineering with AWS
Index
About the Authors
Rathish Mohan is a distinguished applied scientist and AI/ML leader with over a decade of experience in machine learning, natural language processing (NLP), and computer vision. Currently, he is a Senior Applied ML Scientist at Lore | Contagious Health, where he leads cross-disciplinary teams to develop advanced AI systems. Rathish specializes in real-time conversational AI and personalization, leveraging cutting-edge technologies like prefix tuning, LLMs, and RAG pipelines to improve user health and well-being.
Shekhar Agrawal is a seasoned AI and data engineering expert with over 14 years of experience in leading large-scale AI, ML, and NLP initiatives across globally recognized organizations. Currently a Senior Director of Data Science at Oracle Corporation, Shekhar spearheads the development of cutting-edge Generative AI platforms and enterprise-scale machine learning systems that serve thousands of customers worldwide.
Srinivasa Sunil Chippada is a Data Science Engineering expert with 18 years of experience. He offers valuable technical insights to help organizations maximize data value through Feature Stores, Data Marts, Data Pipelines, and Data Integration techniques. His expertise empowers organizations to build efficient and scalable data systems that leverage the full potential of data to drive innovation and business growth.
