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

Summary

In this chapter, we explored a variety of data consumers that you are likely to find in most organizations, including business users, data analysts, and data scientists. We briefly examined their roles, and then looked at the types of AWS services that each of them is likely to use to work with data.

In the hands-on section of this chapter, we took on the role of a data analyst, tasked with creating a mailing list for the marketing department. We used data that had been imported from a MySQL database into S3 in a previous chapter, joined two of the tables from that database, and transformed the data in some of the columns. Then, we wrote the newly transformed dataset out to Amazon S3 as a CSV file.

In the next chapter, Loading Data into a Data Mart, we will look at how data from a data lake can be loaded into a data warehouse, such as Amazon Redshift.