You're reading from Data Engineering with AWS - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Pages 636 pages

Edition 2nd Edition

Languages

Concepts

Data Engineering

Author (1):

Gareth Eagar

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering

3. Data Management Architectures for Analytics

4. The AWS Data Engineer’s Toolkit

5. Data Governance, Security, and Cataloging

6. Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations

7. Architecting Data Engineering Pipelines

8. Ingesting Batch and Streaming Data

9. Transforming Data to Optimize for Analytics

10. Identifying and Enabling Data Consumers

11. A Deeper Dive into Data Marts and Amazon Redshift

12. Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Ad Hoc Queries with Amazon Athena

15. Visualizing Data with Amazon QuickSight

16. Enabling Artificial Intelligence and Machine Learning

17. Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World

18. Building Transactional Data Lakes

19. Implementing a Data Mesh Strategy

20. Building a Modern Data Platform on AWS

21. Wrapping Up the First Part of Your Learning Journey

22. Other Books You May Enjoy

23. Index

Understanding the value of ML and AI for organizations

More and more companies, of all sizes, are in various stages in the journey of discovering how ML and AI can positively impact their business. While initially, only the largest of organizations had the money and expertise to invest in ML projects, over time, the required technology has become more affordable and more accessible to non-specialist developers.

Cloud providers, such as AWS, have played a big part in making ML and AI technology more accessible to a wider group of users. Today, a developer with no previous ML education or experience can use a service such as Amazon Lex to create a customer service chatbot. This chatbot will allow customers to ask questions using natural language, rather than having to select from a menu of preset choices. Not all that long ago, anyone wanting to create a chatbot like this would have needed a Ph.D. in ML!

Many large organizations still look to build up data science teams with...