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Data Engineering with AWS

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

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800560413
Length 482 pages
Edition 1st Edition
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Author (1):
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Gareth Eagar Gareth Eagar
Author Profile Icon Gareth Eagar
Gareth Eagar
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Toc

Table of Contents (19) Chapters Close

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

Technical requirements

In the hands-on sections of this chapter, we will use the Amazon 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 permissions to create an AWS Glue Crawler and permissions to use Amazon Athena to query data.

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

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