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

AWS services for consuming data

Once the data has been transformed and optimized for analytics, the various data consumers in an organization need easy access to the data via a number of different types of interfaces. Data scientists may want to use standard SQL queries to query the data, while data analysts may want to both query the data in place using SQL and also load subsets of the data into a high-performance data warehouse for low-latency, high-concurrency queries and scheduled reporting. Business users may prefer accessing data via a visualization tool that enables them to view data represented as graphs, charts, and other types of visuals.

In this section, we introduce a number of AWS services that enable different types of data consumers to work with our optimized datasets. We don't cover all services that can be used to consume data in this section, but instead highlight the primary services relevant to the data engineering role.

Overview of Amazon Athena for...