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Data Engineering with AWS - Second Edition

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

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Pages 636 pages
Edition 2nd Edition
Languages
Author (1):
Gareth Eagar Gareth Eagar
Profile icon Gareth Eagar

Table of Contents (24) Chapters

Preface Section 1: AWS Data Engineering Concepts and Trends
An Introduction to Data Engineering Data Management Architectures for Analytics The AWS Data Engineer’s Toolkit Data Governance, Security, and Cataloging Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
Architecting Data Engineering Pipelines Ingesting Batch and Streaming Data Transforming Data to Optimize for Analytics Identifying and Enabling Data Consumers A Deeper Dive into Data Marts and Amazon Redshift Orchestrating the Data Pipeline Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
Ad Hoc Queries with Amazon Athena Visualizing Data with Amazon QuickSight Enabling Artificial Intelligence and Machine Learning Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
Building Transactional Data Lakes Implementing a Data Mesh Strategy Building a Modern Data Platform on AWS Wrapping Up the First Part of Your Learning Journey Other Books You May Enjoy
Index

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Qr code Description automatically generated

Data governance, security, and related topics are some of the most important topics to cover in a book that is all about data. Having the most efficient data pipelines, the fastest data transformations, and the best data consumption tools is not worth much if the data is not kept secure and governed correctly. Data must also be stored and accessed in a way that complies with local laws, and the data needs to be cataloged so that it is discoverable and useful to the organization.Sadly, it is not uncommon to read about data breaches and poor data handling by organizations, and the consequences of this can include reputational damage to the organization, as well as potentially massive penalties imposed by the government. It is also not uncommon for organizations to find that they have massive quantities of data, but that they are getting very little value from that data since it is siloed, of poor quality, or users...

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