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Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

€36.99

€48.99

€38.99

Total € 124.97

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Amazon verified reviews

Prabakaran Jun 14, 2022

Amazing content , covered all most everything in DE space. One minor feedback, can also add more content related to Data Governance

Amazon Verified review

PCTriangle Jan 09, 2022

Overall good book! Strategically compiled with a great approach of what it takes to be a data engineer. Good for beginners and experts alike (I’m in the middle). It’s rare to find a technical book where the information is presented logically, sequentially and thoroughly. This book is best to get started quickly on the job; it might not offer enough depth if you’re studying for an AWS certification.

J. Dadd Jan 10, 2022

Buy this book if you want to learn some theory and then be shown how to implement the concepts introduced on AWS. This is not a high-brow textbook heavy on theory. It is great for anyone considering using AWS for data warehousing and data engineering, especially generalist infrastructure architects (like myself) who do not focus purely on this space.Gareth deftly sets the scene by providing a useful review of data warehousing technology over time, and how the modern data management landscape has been shaped. Each section of the book starts with a solid theoretical introduction followed by a clear, useful, and most importantly technically detailed how-to guide for implementing each new concept within the AWS environment.I especially like the section on AI / ML; Gareth and his co-contributors succinctly describe how to knit together multiple AWS services to produce a working end-to-end data analytics solution. There are also invaluable bonus sections, such as how to manage a data engineering project, from identifying stakeholders, running whiteboarding sessions, identifying data sources, pipelines, and toolsets.As stated in the introduction, this book alone will not magically turn you into a data engineer. What it will do, however, is give you a solid theoretical and technical foundation in all key concepts, as well as offer insights into future trends. This book is an invaluable point of reference as you progress on your data engineering journey within the AWS ecosystem.

Christoph Jan 26, 2022

Ein gelungenes Buch zum Thema ETL und Big Data in AWS. Für meinen Geschmack genau die richtige Mischung zwischen Breite und Tiefe mit praxisnahen Anwendungsfällen - das sage ich als erfahrener Cloud Engineer.

SANTI TEERAGUL Aug 20, 2022

That’s great for my new job using AWS

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

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