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

Product type Paperback

Published in Jun 2020

Publisher Packt

ISBN-13 9781838646677

Length 810 pages

Edition 1st Edition

Tools

Office 365

Concepts

Project Management

Authors (2):

Gaurav Mahajan

Sudeep Ghatak

View More author details

Table of Contents (19) Chapters

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

Understanding QuickSight's advanced features – ML Insights and embedded dashboards

The enterprise edition of Amazon QuickSight includes two advanced features that can help you draw out additional insights from your data, and that can enable you to widely share your data by embedding dashboards into applications.

Amazon QuickSight ML Insights

QuickSight ML Insights uses the power of machine learning algorithms to automatically uncover insights and trends, forecast future data points, and identify anomalies in your data.

All of these ML Insights can be easily added to an analysis/dashboard without the author needing to have any machine learning experience or any real understanding of the underlying ML algorithms. However, for those who are interested in the underlying ML algorithms used by QuickSight, Amazon provides comprehensive documentation on this topic. Review the Amazon QuickSight documentation titled Understanding the ML Algorithim Used by Amazon QuickSight...