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

Federating the queries of external data sources with Amazon Athena Query Federation

As we've discussed several times in this book, Athena lets you query data that has been loaded into the data lake using standard SQL semantics. But since the launch of Athena, AWS has added additional functionality to enhance Athena to make it an even more powerful query engine.

One of those major enhancements, which became available in 2021 with Athena query engine v2, was the ability to run federated queries, which we will look at next.

Querying external data sources using Athena Federated Query

Query federation, also sometimes referred to as data virtualization, is the process of querying multiple external data sources, in different database engines or other systems, through a single SQL query statement. In November 2019, AWS announced the preview of Federated Query in Amazon Athena, which enables a single Athena query to query data in data lakes, as well as data from external sources...