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

Loading data into data marts

Many tools can work directly with data in the data lake, as we covered in Chapter 3, The AWS Data Engineer's Toolkit. These include tools for ad hoc SQL queries (Amazon Athena), data processing tools (such as Amazon EMR and AWS Glue), and even specialized machine learning tools (such as Amazon SageMaker).

These tools read data directly from Amazon S3, but there are times where a use case may require much lower latency, higher performance reads of the data. Or, there may be times where the use of highly structured schemas may best meet the analytic requirements of the use case. In these cases, loading data from the data lake into a data mart makes sense.

In analytic environments, a data mart is most often a data warehouse system (such as Amazon Redshift), but it could also be a relational database system (such as Amazon RDS MySQL), depending on the use case's requirements. In either case, the system will have local storage (often high-speed...