You're reading from Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781800560413

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Author (1):

Gareth Eagar

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

Ingesting and preparing data from a variety of sources

Amazon QuickSight can use other AWS services as a source, as well as on-premises databases, imported files, and even some Software as a Service (SaaS) applications.

For example, you can easily connect to Oracle, Microsoft SQL Server, Postgres, and MySQL databases, either running as part of the Amazon RDS managed database service or as instances running on Amazon EC2, or in your own data centers. You can also connect to data warehouse systems such as Amazon Redshift, Snowflake, and Teradata. Other AWS services are also supported as data sources, including Amazon S3, Amazon Athena, Amazon ElasticSearch Service, Amazon Aurora, and AWS IoT Analytics.

In addition to these traditional data sources, QuickSight can also connect to various SaaS offerings, including ServiceNow, Jira, Adobe Analytics, Salesforce, GitHub, and Twitter.

Data stored in files, such as a Microsoft Excel Spreadsheet (XLSX files), JSON documents, and...