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Data Engineering with AWS

You're reading from   Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800560413
Length 482 pages
Edition 1st Edition
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Author (1):
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Gareth Eagar Gareth Eagar
Author Profile Icon Gareth Eagar
Gareth Eagar
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Table of Contents (19) Chapters Close

Preface 1. Section 1: AWS Data Engineering Concepts and Trends
2. Chapter 1: An Introduction to Data Engineering FREE CHAPTER 3. Chapter 2: Data Management Architectures for Analytics 4. Chapter 3: The AWS Data Engineer's Toolkit 5. Chapter 4: Data Cataloging, Security, and Governance 6. Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
7. Chapter 5: Architecting Data Engineering Pipelines 8. Chapter 6: Ingesting Batch and Streaming Data 9. Chapter 7: Transforming Data to Optimize for Analytics 10. Chapter 8: Identifying and Enabling Data Consumers 11. Chapter 9: Loading Data into a Data Mart 12. Chapter 10: Orchestrating the Data Pipeline 13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
14. Chapter 11: Ad Hoc Queries with Amazon Athena 15. Chapter 12: Visualizing Data with Amazon QuickSight 16. Chapter 13: Enabling Artificial Intelligence and Machine Learning 17. Chapter 14: Wrapping Up the First Part of Your Learning Journey 18. Other Books You May Enjoy

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 specialized...

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