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

Identifying data sources and ingesting data

With an understanding of the overall business goals for the project, and having identified our data consumers, we can start exploring the available data sources.

While most data sources will be internal to the organization, some projects may require enriching organization-owned data with other third-party data sources. Today, there are many data marketplaces where diverse datasets can be subscribed to, or in some cases, accessed for free. When discussing data sources, both internal and external datasets should be considered.

The team that has been included in the workshop should include people that understand the data sources required for the project. Some of the information that the data engineer needs to gather about these data sources includes the following:

  • Details about the source system containing the data (is the data in a database, in files on a server, existing files on Amazon S3, coming from a streaming source, and...