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

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 Glue job that joins the streaming data with the 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 the 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, written 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: