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

Transformations – making raw data more valuable

As we have discussed in various places throughout this book, data can be one of the most valuable assets that an organization owns. However, raw, siloed data has limited value on its own, and we unlock the real value of an organization's data when we combine various raw datasets and transform that data through an analytics pipeline.

Cooking, baking, and data transformations

Look at the following list of food items and consider whether you enjoy eating them:

  • Sugar
  • Butter
  • Eggs
  • Milk

For many people, these are pretty standard food items, and some (like the eggs and milk) may be consumed on their own, while others (like the sugar and the butter) are generally consumed with something else, such as adding sugar to your coffee or tea, or spreading butter on bread.

But, if you take those items and add a few more (like flour and baking powder) and combine all the items in just the right way, you could...