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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
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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

A deeper dive into data warehouse concepts and architecture

An Enterprise Data Warehouse (EDW) is the central data repository that contains structured, curated, consistent, and trusted data assets that are organized into a well-modeled schema. The data assets in an EDW are made up of all the relevant information about key business domains and are built by integrating data sourced from the following places:

  • Run-the-business transactional applications (ERPs, CRMs, Line of Business applications) that support all the key business domains across the enterprise.
  • External data sources such as data from partners and third parties.

An enterprise data warehouse provides business users and decision-makers with an easy-to-use, central platform that helps them find and analyze a well-modeled, well-integrated, single version of truth about various business subject areas such as customer, product, sales, marketing, supply chain, and more. Business users analyze data in the warehouse to measure business...

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