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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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Toc

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

Chapter 3: The AWS Data Engineer's Toolkit

Back in 2006, Amazon launched Amazon Web Services (AWS) to offer on-demand delivery of IT resources over the internet, essentially creating the cloud computing industry. Ever since then, AWS has been innovating at an incredible pace, continually launching new services and features to offer broad and deep functionality across a wide range of IT services.

Traditionally organizations built their own big data processing systems in their data centers, implementing commercial or open source solutions designed to help them make sense of ever-increasing quantities of data. However, these systems were often complex to install, requiring a team of people to maintain, optimize, and update, and scaling these systems was a challenge, requiring large infrastructure spend and significant delays while waiting for hardware vendors to install new compute and storage systems.

Cloud computing has enabled the removal of many of these challenges, including...

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