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

Business use case transforms

In a data lake environment, you generally ingest data from many different source systems into a landing, or raw, zone. You then optimize the file format and partition the dataset, as well as applying cleansing rules to the data, potentially now storing the data in a different zone, often referred to as the clean zone. At this point, you may also apply updates to the dataset with CDC-type data and create the latest view of the data, which we examine in the next section.

The initial transforms we covered in the previous section could be completed without needing to understand too much about how the data is going to ultimately be used by the business. At that point, we were still working on individual datasets that will be used by downstream transformation pipelines to ultimately prepare the data for business analytics.

But at some point, you, or another data engineer working for a line of business, are going to need to use a variety of these ingested...