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  • Book Overview & Buying Amazon Redshift Cookbook
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Amazon Redshift Cookbook

Amazon Redshift Cookbook

By : Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel
4.8 (9)
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Amazon Redshift Cookbook

Amazon Redshift Cookbook

4.8 (9)
By: Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel

Overview of this book

Amazon Redshift is a fully managed, petabyte-scale AWS cloud data warehousing service. It enables you to build new data warehouse workloads on AWS and migrate on-premises traditional data warehousing platforms to Redshift. This book on Amazon Redshift starts by focusing on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll then learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. Because of the massive amount of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of Redshift's columnar architecture and managed services. As you advance, you’ll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, which help minimize the operational efforts that you have to invest in managing regular ETL pipelines and ensure the timely and accurate refreshing of your data warehouse. Finally, you'll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform. By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems.
Table of Contents (13 chapters)
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Managing views in a database

View database objects allow the result of a query to be stored. In Amazon Redshift, views run each time the view is mentioned in a query. The advantage of using a view instead of a table is that it can allow access to only a subset of data on a table, join more than one table into a single virtual table, and act as an aggregated table, and it takes up no space on the database since only the definition is saved, hence making it convenient to abstract complicated queries. In this recipe, we will create views to store queries for the underlying tables.

Getting ready

To complete this recipe, you will need everything mentioned in the Technical requirements section at the start of the chapter.

How to do it…

Let’s create a view using the CREATE VIEW command. We will use the following steps to create a view:

  1. Create a finance.customer_vw view based on the results of the query on finance.customer:
    CREATE VIEW finance...
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Amazon Redshift Cookbook
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