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

AWS services for transforming data

Once your data is ingested into an appropriate AWS service, such as Amazon S3, the next stage of the pipeline needs to transform the data to optimize it for analytics and to make it available to your data consumers.

Some of the tools we discussed in the previous section for ingesting data into AWS can perform light transformations as part of the ingestion process. For example, Amazon DMS can write out data in Parquet format (a format optimized for analytics), as can Kinesis Firehose. However, heavier transformations are often required to fully optimize your data for a differing set of analytic tasks and diverse data consumers, and in this section, we will examine some of the core AWS services that can be used for this.

Overview of AWS Lambda for light transformations

AWS Lambda provides a serverless environment for executing code and is one of AWS's most popular services. You can trigger your Lambda function to execute your code in multiple...