Data Engineering with AWS

person icon Packt Publishing

Data Engineering with AWS

Data Engineering with AWS

person icon Gareth Eagar

ebook icon Packt Publishing

language icon Language - English

updated on icon Updated on Jan, 2023

architecture icon Amazon AWS,Development,Cloud Computing


This eBook includes

Formats : PDF (Read Only)

Pages : 482

ISBN : 9781800569041

Language : English

About the Book

Book description

The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly

Key Features

  • Learn about common data architectures and modern approaches to generating value from big data
  • Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines
  • Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert

Book Description

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.

What you will learn

  • Understand data engineering concepts and emerging technologies
  • Ingest streaming data with Amazon Kinesis Data Firehose
  • Optimize, denormalize, and join datasets with AWS Glue Studio
  • Use Amazon S3 events to trigger a Lambda process to transform a file
  • Run complex SQL queries on data lake data using Amazon Athena
  • Load data into a Redshift data warehouse and run queries
  • Create a visualization of your data using Amazon QuickSight
  • Extract sentiment data from a dataset using Amazon Comprehend

Who this book is for

This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful.

A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

Data Engineering with AWS

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Author Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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