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You're reading from  Data Engineering with AWS - Second Edition

Product typeBook
Published inOct 2023
PublisherPackt
ISBN-139781804614426
Edition2nd Edition
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Author (1)
Gareth Eagar
Gareth Eagar
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Gareth Eagar

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.
Read more about Gareth Eagar

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The benefits of the cloud when building big data analytic solutions

For a long time, organizations relied on complex systems that they would run in their own data centers to help them capture, store, and process large amounts of data. But over the last decade or so, there has been a trend of an increasing amount of data that organizations want to store and analyze, and on-premises systems have struggled to scale to keep up with demand. Scaling up these traditional tools for managing ever-increasing dataset sizes has been expensive, complex, and time-consuming, and organizations have been seeking alternative solutions to cope with the increasing data volumes.

Ever since Amazon launched AWS in 2006, organizations have been realizing the benefits of running their workloads in the cloud. Cloud computing enables scalability, cost efficiency, security, and automation that most companies find impossible to achieve within their own data centers, and this applies to the area of data analytics as well. One of the first AWS services was Amazon Simple Storage Service (Amazon S3), a cloud-based object store that offers essentially unlimited scalability at low cost, and yet provides durability and availability that most data center managers could only dream of achieving. Today, Amazon S3 has become the physical storage layer for many thousands of data lake projects, and a wide ecosystem of analytic tools has been created to work with the service.

Successful data engineers need to understand the tools available in the cloud for building out complex data analytic projects and understand which set of tools is best to achieve the outcome needed for their project. In this book, you will learn more about AWS services for working with big data, and you will gain hands-on experience in developing a data engineering pipeline in AWS.

To get started, you will need either an existing AWS account, or you will need to create a new AWS account so that you can follow along with the practical examples. In the next section, we provide step-by-step instructions for creating a new AWS account.

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Published in: Oct 2023Publisher: PacktISBN-13: 9781804614426
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Author (1)

author image
Gareth Eagar

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.
Read more about Gareth Eagar