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 consumers and understanding their requirements

A typical organization is likely to have multiple different categories, or types, of data consumers. We discussed some of these roles in Chapter 1, An Introduction to Data Engineering, but let's review these again:

  • Business users: A business user generally wants to access data via interactive dashboards and other visualization types. For example, a sales manager may want to see a chart showing last week's sales by sales rep, geographic area, or top product categories.
  • Business applications: In some use cases, the data pipeline that the data engineer builds will be used to power other business applications. For example, Spotify, the streaming music application, provides users with an in-app summary of their listening habits at the end of each year (top songs, top genres, total hours of music streamed, and so on). Read the following Spotify blog post to learn more about how the Spotify data team enabled...