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

Representing data visually for maximum impact

Data lakes are designed to capture large amounts of raw data and enable the processing of that data to draw out new insights that provide business value. The insights that are gained from a data lake can be represented in many ways, such as reports that summarize sales data and top sales items, machine learning (ML) models that can predict future trends, and visualizations and dashboards that effectively summarize data. Each of these ways of representing data offers different benefits, depending on the business purpose:

  • If you're a data analyst that needs to report sales figures, profit margins, inventory levels, and other data for each category of product the company produces, you would probably want access to detailed tabular data. You would want the power of SQL to run powerful queries against the data to draw varied insights so that you can provide this data to different departments within the organization.
  • If you...