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 – joining datasets with AWS Glue Studio

For our hands-on exercise in this chapter, we are going to use AWS Glue Studio to create an Apache Spark job that joins streaming data with data we migrated from our MySQL database in the previous chapter.

Creating a new data lake zone – the curated zone

As discussed in Chapter 2, Data Management Architecture for Analytics, it is common to have multiple zones in a data lake, containing different copies of our data as it gets transformed. So far, we have ingested raw data into the landing zone and then converted some of those datasets into Parquet format, and written the files out in the clean zone. In this chapter, we will be joining multiple datasets together and will write out the new dataset to the curated zone of our data lake. The curated zone is intended to store data that has been transformed and is ready for consumption by data consumers. We created an Amazon S3 bucket for the curated zone in a previous...