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Data Engineering with AWS

Data Engineering with AWS

By : Gareth Eagar
4.7 (24)
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Data Engineering with AWS

Data Engineering with AWS

4.7 (24)
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)
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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

Chapter 2: Data Management Architectures for Analytics

In Chapter 1, An Introduction to Data Engineering, we looked at the challenges introduced by ever-growing datasets, and how the cloud can help solve these analytical challenges. However, there are many different cloud services, open source frameworks, and architectures that can be used in analytical projects, depending on business requirements and objectives.

In this chapter, we will discuss how analytical technologies have evolved and introduce the key technologies and concepts that are foundational for building modern analytical architectures, irrespective of whether you build them on AWS or elsewhere.

If you have experience as a data engineer and have worked with enterprise data warehouses before, you may want to skim through the sections of this chapter, and then do the hands-on exercise at the end of this chapter. However, if you are new to data engineering and do not have experience with big data analytics, then the...

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