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

Identifying data transformations and optimizations

In a typical data analytics project, we ingest data from multiple data sources and then perform transforms on those datasets to optimize them for the required analytics.

In Chapter 7, Transforming Data to Optimize for Analytics we will do a deeper dive into typical transformations and optimizations, but we will provide a high-level overview of the most common transformations here.

File format optimizations

CSV, XML, JSON, and other types of plaintext files are commonly used to store structured and semi-structured data. These file formats are useful when manually exploring data, but there are much better, binary-based file formats to use for computer-based analytics. A common binary format that is optimized for read-heavy analytics is the Apache Parquet format. A common transformation is to convert plaintext files into an optimized format, such as Apache Parquet.