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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

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 innovated 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 significant expenditure on infrastructure and 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 the...

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