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

Meeting the needs of business users with data visualization

Some roles within an organization, such as data analysts, have always had easy access to data. For a long time, these roles were effectively gatekeepers of the data, and any "ordinary" business users that had custom data requirements would need to go through the data gatekeepers.

However, over the past few years, the growth of big data has expanded the thirst and need for custom data among a growing number of business users. Business users are no longer willing to tolerate having to go through long, formal processes to access the data they need to make decisions. Instead, users have come to demand easier, and more immediate, access to wider sets of data.

To remain competitive, organizations need to ensure that they enable all the decision-makers in their business to have easy and direct access to the right data. At the same time, organizations need to ensure that good data governance is in place, and that data...