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Data Engineering with AWS

Data Engineering with AWS

By : Eagar
4.7 (24)
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Data Engineering with AWS

Data Engineering with AWS

4.7 (24)
By: 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 8: Identifying and Enabling Data Consumers

A data consumer can be defined as a person, or application, within an organization that needs access to data. Data consumers can vary from staff that pack shelves and need to know stock levels, to the CEO of an organization that needs data to make a decision on which projects to invest in. A data consumer can also be a system that needs data from a different system.

Everything a data engineer does is to make datasets useful and accessible to data consumers, which, in turn, enables the business to gain useful insights from their data. This means delivering the right data, via the right tools, to the right people or applications, at the right time, to enable the business to make informed decisions.

Therefore, when designing a data engineering pipeline (as covered in Chapter 5, Architecting Data Engineering Pipelines), data engineers should start by understanding business objectives, including who the data consumers are and what...

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