Book Image

Data Engineering with AWS - Second Edition

By : Gareth Eagar
5 (1)
Book Image

Data Engineering with AWS - Second Edition

5 (1)
By: Gareth Eagar

Overview of this book

This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!
Table of Contents (24 chapters)
1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
17
Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
22
Other Books You May Enjoy
23
Index

The benefits of the cloud when building big data analytic solutions

For a long time, organizations relied on complex systems that they would run in their own data centers to help them capture, store, and process large amounts of data. But over the last decade or so, there has been a trend of an increasing amount of data that organizations want to store and analyze, and on-premises systems have struggled to scale to keep up with demand. Scaling up these traditional tools for managing ever-increasing dataset sizes has been expensive, complex, and time-consuming, and organizations have been seeking alternative solutions to cope with the increasing data volumes.

Ever since Amazon launched AWS in 2006, organizations have been realizing the benefits of running their workloads in the cloud. Cloud computing enables scalability, cost efficiency, security, and automation that most companies find impossible to achieve within their own data centers, and this applies to the area of data analytics as well. One of the first AWS services was Amazon Simple Storage Service (Amazon S3), a cloud-based object store that offers essentially unlimited scalability at low cost, and yet provides durability and availability that most data center managers could only dream of achieving. Today, Amazon S3 has become the physical storage layer for many thousands of data lake projects, and a wide ecosystem of analytic tools has been created to work with the service.

Successful data engineers need to understand the tools available in the cloud for building out complex data analytic projects and understand which set of tools is best to achieve the outcome needed for their project. In this book, you will learn more about AWS services for working with big data, and you will gain hands-on experience in developing a data engineering pipeline in AWS.

To get started, you will need either an existing AWS account, or you will need to create a new AWS account so that you can follow along with the practical examples. In the next section, we provide step-by-step instructions for creating a new AWS account.