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Data Engineering with AWS

Data Engineering with AWS - Second Edition

By : Gareth Eagar
4.8 (31)
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Data Engineering with AWS

Data Engineering with AWS

4.8 (31)
By: Gareth Eagar

Overview of this book

This book, authored by a Senior Data Architect with 25 years of experience, helps you gain expertise in the AWS ecosystem for data engineering. This revised edition updates every chapter to cover the latest AWS services and features, provides a refreshed view on data governance, and introduces a new section on building modern data platforms. You will learn how to implement a data mesh, work with open-table formats such as Apache Iceberg, and apply DataOps practices for automation and observability. You will begin by exploring core concepts and essential AWS tools used by data engineers, along with modern data management approaches. You will then design and build data pipelines, review raw data sources, transform data, and understand how it is consumed by various stakeholders. The book also covers data governance, populating data marts and warehouses, and how a data lakehouse fits into the architecture. You will explore AWS tools for analysis, SQL queries, visualizations, and learn how AI and machine learning generate insights from data. Later chapters cover transactional data lakes, data meshes, and building a complete AWS data platform. By the end, you will be able to confidently implement data engineering pipelines on AWS. *Email sign-up and proof of purchase required
Table of Contents (24 chapters)
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1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
17
Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
22
Other Books You May Enjoy
23
Index

Summary

In this chapter, we reviewed how data is becoming ever more important for organizations looking to gain new insights and competitive advantage, and introduced some core big data processing technologies and approaches. We also looked at the key roles related to managing, processing, and analyzing large datasets, and highlighted how cloud technologies enable organizations to better deal with the increasing volume and variety of data.

In our first hands-on exercise, we provided step-by-step instructions for creating a new AWS account, which can be used through the remainder of this book as we develop our own data engineering pipeline.

In the next chapter, we dig deeper into current approaches, tools, and frameworks that are commonly used to manage and analyze large datasets, including data warehouses, data marts, data lakes, and data mesh. We also get hands-on with AWS again, but this time we will use the AWS Command Line Interface (CLI) to create new Amazon S3 buckets.

Learn more on Discord

To join the Discord community for this book – where you can share feedback, ask questions to the author, and learn about new releases – follow the QR code below:

https://discord.gg/9s5mHNyECd

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Data Engineering with AWS
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