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Data Engineering with AWS

You're reading from   Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Length 636 pages
Edition 2nd Edition
Tools
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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 (24) Chapters Close

Preface 1. Section 1: AWS Data Engineering Concepts and Trends
2. An Introduction to Data Engineering FREE CHAPTER 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...

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