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

Wrapping up the whiteboarding session

After completing the whiteboarding session, you should have a high-level overview architecture that illustrates the main components of the pipeline that you plan to build. At this point, there will still be a lot of questions that have been left unanswered and there will not be a lot of specific detail. However, the high-level architecture should be enough to get broad agreement from stakeholders on the proposed plans for the project. It should have also provided you with enough information that you can start on a detailed design and set up follow-up sessions as required.

Some of the information that you should have after the session includes the following:

  • A good understanding of who the data consumers for this project will be
  • For each category of data consumer, a good idea of what type of tools they would use to access the data (SQL, visualization tools, and so on)
  • An understanding of the internal and external data sources...