You're reading from Data Engineering with AWS - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Pages 636 pages

Edition 2nd Edition

Languages

Concepts

Data Engineering

Author (1):

Gareth Eagar

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering

3. Data Management Architectures for Analytics

4. The AWS Data Engineer’s Toolkit

5. Data Governance, Security, and Cataloging

6. Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations

7. Architecting Data Engineering Pipelines

8. Ingesting Batch and Streaming Data

9. Transforming Data to Optimize for Analytics

10. Identifying and Enabling Data Consumers

11. A Deeper Dive into Data Marts and Amazon Redshift

12. Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Ad Hoc Queries with Amazon Athena

15. Visualizing Data with Amazon QuickSight

16. Enabling Artificial Intelligence and Machine Learning

17. Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World

18. Building Transactional Data Lakes

19. Implementing a Data Mesh Strategy

20. Building a Modern Data Platform on AWS

21. Wrapping Up the First Part of Your Learning Journey

22. Other Books You May Enjoy

23. Index

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 OpenSearch 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 CSV...