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  • Book Overview & Buying Amazon Redshift Cookbook
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Amazon Redshift Cookbook

Amazon Redshift Cookbook - Second Edition

By : Shruti Worlikar, Harshida Patel, Anusha Challa
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Amazon Redshift Cookbook

Amazon Redshift Cookbook

By: Shruti Worlikar, Harshida Patel, Anusha Challa

Overview of this book

Amazon Redshift Cookbook offers comprehensive guidance for leveraging AWS's fully managed cloud data warehousing service. Whether you're building new data warehouse workloads or migrating traditional on-premises platforms to the cloud, this essential resource delivers proven implementation strategies. Written by AWS specialists, these easy-to-follow recipes will equip you with the knowledge to successfully implement Amazon Redshift-based data analytics solutions using established best practices. The book focuses on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. The book covers recipes to help you take full advantage of Redshift's columnar architecture and managed services. You'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, helping you minimize the operational effort that you invest in managing regular ETL pipelines and ensuring the timely and accurate refreshing of your data warehouse. By the end of the Redshift book, you'll be able to implement a Redshift-based data analytics solution by adopting best-practice approaches for solving commonly faced problems. *Email sign-up and proof of purchase required
Table of Contents (17 chapters)
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14
Other Books You May Enjoy
15
Index

Implementing dynamic data masking

Dynamic data masking (DDM) (https://docs.aws.amazon.com/redshift/latest/dg/t_ddm.html) lets you hide sensitive data. It is often used to meet regulations or privacy standards. DDM allows you to control how sensitive data is displayed, based on a user’s permissions. This is done at the time the data is accessed, not when it is stored. DDM is an alternative to permanently obscuring the data during the data loading process. With DDM, you don’t need to modify your data pipelines. You set up masking policies that determine who can see what data. These policies are attached to tables and columns. The policies apply to individual users, roles, or everyone. DDM makes it easier to adapt to changing privacy requirements, without needing to change your data transformations, underlying data, or application queries.

Using DDM and Amazon Redshift Lambda User-Defined Functions (https://docs.aws.amazon.com/redshift/latest/dg/udf-creating-a-lambda...

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Amazon Redshift Cookbook
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