Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

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.

1140656101
Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

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.

51.99 In Stock
Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

by Gareth Eagar
Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

by Gareth Eagar

eBook

$51.99 

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Overview

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.


Product Details

ISBN-13: 9781800569041
Publisher: Packt Publishing
Publication date: 12/29/2021
Sold by: Barnes & Noble
Format: eBook
Pages: 482
File size: 15 MB
Note: This product may take a few minutes to download.

About the Author

Gareth Eagar has over 25 years of experience in the IT industry, starting in South Africa, working in the United Kingdom for a while, and now based in the USA. Having worked at AWS since 2017, Gareth has broad experience with a variety of AWS services, and deep expertise around building data platforms on AWS. While Gareth currently works as a Solutions Architect, he has also worked in AWS Professional Services, helping architect and implement data platforms for global customers. Gareth frequently speaks on data related topics.

Table of Contents

Table of Contents
  1. An Introduction to Data Engineering
  2. Data Management Architectures for Analytics
  3. The AWS Data Engineer's Toolkit
  4. Data Cataloging, Security and Governance
  5. Architecting Data Engineering Pipelines
  6. Ingesting Batch and Streaming Data
  7. Transforming Data to Optimize for Analytics
  8. Identifying and Enabling Data Consumers
  9. Loading Data into a Data Mart
  10. Orchestrating the Data Pipeline
  11. Ad Hoc Queries with Amazon Athena
  12. Visualizing Data with Amazon QuickSight
  13. Enabling Artificial Intelligence and Machine Learning
  14. Wrapping Up the First Part of Your Learning Journey
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