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

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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

Technical requirements

In the hands-on sections of this chapter, we will use the AWS Database Migration Service (DMS) service to ingest data from a database source, and then we will ingest streaming data using Amazon Kinesis. To ingest data from a database, you need IAM permissions that allow your user to create an RDS database, an EC2 instance, a DMS instance, and a new IAM role and policy.

For the hands-on section on ingesting streaming data, you will need IAM permissions to create a Kinesis Data Firehose instance, as well as permissions to deploy a CloudFormation template. The CloudFormation template that is deployed will create IAM roles, a Lambda function, as well as Amazon Cognito users and other Cognito resources.

To query the newly ingested data, you will need permission to create an AWS Glue Crawler and permission to use Amazon Athena to query data.

You can find the code files of this chapter in the GitHub repository at the following link: https://github.com/PacktPublishing...