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

Getting data security and governance right

Data security dictates how an organization should protect data to ensure that data is stored securely (such as in an encrypted state) and that access by unauthorized entities is prevented. For example, all the things an organization does to prevent falling victim to a ransomware attack, or having their data stolen and sold on the dark web, falls under data security.

Data governance, on the other hand, is related to ensuring that only people that need access to specific datasets have that access (such as ensuring that data is not just generally open to all users of a system without considering whether they need access to that data to perform their job). Governance also applies to ensuring that an organization only uses and processes data on individuals in approved ways and that organizations provide data disclosures as required by law.

Not providing adequate protection and security of an organization's data, or not complying with...