You're reading from Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Length 636 pages

Edition 2nd Edition

Tools

AWS

Concepts

Data Engineering

Author (1):

Gareth Eagar

View More author details

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering FREE CHAPTER

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

Hands-on – ingesting data with AWS DMS

As we discussed earlier in this chapter, AWS DMS can be used to replicate a database into an Amazon S3-based data lake (among other uses). Follow the steps in this section to do the following:

Deploy a CloudFormation template that configures a MySQL RDS instance and then deploys an EC2 instance to load a demo database into MySQL.
Set up a DMS replication instance and configure endpoints and tasks.
Run the DMS instance in full-load mode.
Run a Glue Crawler to add the tables that were newly loaded into S3 into the AWS Glue Data Catalog.
Query the data with Amazon Athena.
Delete the CloudFormation template in order to remove the resources that have been deployed.

NOTE

The following steps assume the use of your AWS account’s default VPC and security group. You will need to modify the steps as needed if you’re not using the default.