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

Data preparation transformations

The first set of transformations that we look at are those that help prepare the data for further transformations later in the pipeline. These transformations are designed to apply relatively generic optimizations to individual datasets that we are ingesting into the data lake. For these optimizations, you may need some understanding of the source data system and context, but, generally, you do not need to understand the ultimate business use case for the dataset.

Protecting PII data

Often, datasets that we ingest may contain personally identifiable information (PII) data, and there may be governance restrictions on which PII data can be stored in the data lake. As a result, we need to have a process that protects the PII data as soon as possible after it is ingested.

There are a number of common approaches that can be used here (such as tokenization or hashing), each with its own advantages and disadvantages, as we discussed in more detail...