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 3: The AWS Data Engineer's Toolkit

Back in 2006, Amazon launched Amazon Web Services (AWS) to offer on-demand delivery of IT resources over the internet, essentially creating the cloud computing industry. Ever since then, AWS has been innovating at an incredible pace, continually launching new services and features to offer broad and deep functionality across a wide range of IT services.

Traditionally organizations built their own big data processing systems in their data centers, implementing commercial or open source solutions designed to help them make sense of ever-increasing quantities of data. However, these systems were often complex to install, requiring a team of people to maintain, optimize, and update, and scaling these systems was a challenge, requiring large infrastructure spend and significant delays while waiting for hardware vendors to install new compute and storage systems.

Cloud computing has enabled the removal of many of these challenges, including...