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Microsoft 365 and SharePoint Online Cookbook

You're reading from   Microsoft 365 and SharePoint Online Cookbook Over 100 practical recipes to help you get the most out of Office 365 and SharePoint Online

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838646677
Length 810 pages
Edition 1st Edition
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Authors (2):
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Gaurav Mahajan Gaurav Mahajan
Author Profile Icon Gaurav Mahajan
Gaurav Mahajan
Sudeep Ghatak Sudeep Ghatak
Author Profile Icon Sudeep Ghatak
Sudeep Ghatak
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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 5: Architecting Data Engineering Pipelines

Having gained an understanding of data engineering principles, the core concepts, and the available AWS tools, we can now put these together in the form of a data pipeline. A data pipeline is the process that ingests data from multiple sources, optimizes and transforms the data, and makes it available to data consumers. An important function of the data engineering role is the ability to design, or architect, these pipelines.

In this chapter, we will cover the following topics:

  • Approaching the task of architecting a data pipeline
  • Identifying data consumers and understanding their requirements
  • Identifying data sources and ingesting data
  • Identifying data transformations and optimizations
  • Loading data into data marts
  • Wrapping up the whiteboarding session
  • Hands-on – architecting a sample pipeline
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