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

Chapter 7: Transforming Data to Optimize for Analytics

In previous chapters, we covered how to architect a data pipeline and common ways of ingesting data into a data lake. We now turn to the process of transforming raw data in order to optimize the data for analytics and to create value for an organization.

Transforming data to optimize for analytics and to create value for an organization is one of the key tasks for a data engineer, and there are many different types of transformations. Some transformations are common and can be generically applied to a dataset, such as converting raw files to Parquet format and partitioning the dataset. Other transformations use business logic in the transformations and vary based on the contents of the data and the specific business requirements.

In this chapter, we review some of the engines that are available in AWS for performing data transformations and also discuss some of the more common data transformations. However, this book focuses...