Curriculum

16 Chapters · 16 Lessons

  1. 1

    Book Preview

    1. Ultimate AWS Data Engineering Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Unveiling the Secrets of Data Engineering

    1. (Included in full purchase)

      Unveiling the Secrets of Data Engineering

  4. 4

    Chapter 2 : Architecting for Scalability: Data Replication Techniques

    1. (Included in full purchase)

      Architecting for Scalability: Data Replication Techniques

  5. 5

    Chapter 3 : Partitioning and Sharding: Optimizing Data Management

    1. (Included in full purchase)

      Partitioning and Sharding: Optimizing Data Management

  6. 6

    Chapter 4 : Ensuring Consistency: Consensus Mechanisms and Models

    1. (Included in full purchase)

      Ensuring Consistency: Consensus Mechanisms and Models

  7. 7

    Chapter 5 : Balancing the Load: Achieving Performance and Efficiency

    1. (Included in full purchase)

      Balancing the Load: Achieving Performance and Efficiency

  8. 8

    Chapter 6 : Building Fault-Tolerant Architectures

    1. (Included in full purchase)

      Building Fault-Tolerant Architectures

  9. 9

    Chapter 7 : Exploring the Realm of AWS Data Storage Services

    1. (Included in full purchase)

      Exploring the Realm of AWS Data Storage Services

  10. 10

    Chapter 8 : Orchestrating Data Flow

    1. (Included in full purchase)

      Orchestrating Data Flow

  11. 11

    Chapter 9 : Advanced Data Pipelines and Transformation

    1. (Included in full purchase)

      Advanced Data Pipelines and Transformation

  12. 12

    Chapter 10 : Data Warehousing Demystified

    1. (Included in full purchase)

      Data Warehousing Demystified

  13. 13

    Chapter 11 : Visualizing the Unseen

    1. (Included in full purchase)

      Visualizing the Unseen

  14. 14

    Chapter 12 : AWS Machine Learning: Classic AI to Generative AI

    1. (Included in full purchase)

      AWS Machine Learning: Classic AI to Generative AI

  15. 15

    Chapter 13 : Advanced Data Engineering with AWS

    1. (Included in full purchase)

      Advanced Data Engineering with AWS

  16. 16

    INDEX

    1. (Included in full purchase)

      INDEX

About the course

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.

About the Author

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. Known for building scalable AI governance frameworks and integrating technologies such as Kubernetes and Spark, Shekhar has held impactful roles at IQVIA, Comcast, and AOL. 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.