Book Image

Data Engineering with AWS

By : Gareth Eagar
Book Image

Data Engineering with AWS

By: Gareth Eagar

Overview of this book

Written by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.
Table of Contents (19 chapters)
1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

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:

  1. Log into the AWS Management Console (https://console.aws.amazon.com...