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Amazon Redshift Cookbook

You're reading from   Amazon Redshift Cookbook Recipes for building modern data warehousing solutions

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800569683
Length 384 pages
Edition 1st Edition
Languages
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Authors (3):
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Shruti Worlikar Shruti Worlikar
Author Profile Icon Shruti Worlikar
Shruti Worlikar
Harshida Patel Harshida Patel
Author Profile Icon Harshida Patel
Harshida Patel
Thiyagarajan Arumugam Thiyagarajan Arumugam
Author Profile Icon Thiyagarajan Arumugam
Thiyagarajan Arumugam
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Getting Started with Amazon Redshift 2. Chapter 2: Data Management FREE CHAPTER 3. Chapter 3: Loading and Unloading Data 4. Chapter 4: Data Pipelines 5. Chapter 5: Scalable Data Orchestration for Automation 6. Chapter 6: Data Authorization and Security 7. Chapter 7: Performance Optimization 8. Chapter 8: Cost Optimization 9. Chapter 9: Lake House Architecture 10. Chapter 10: Extending Redshift's Capabilities 11. Other Books You May Enjoy Appendix

Managing views

View database objects allow the result of a query to be stored. In Amazon Redshift, views run each time a 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 in 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 access to any SQL interface such as a SQL client or query editor.

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.customer_vw 
    AS
    SELECT customer_number,
           first_name,
           last_name,
           EXTRACT(year FROM date_of_birth) AS year_of_birth
    FROM finance.customer;
  2. To verify that a view has been created, you can use the following command:
    SELECT table_schema as schema_name,
           table_name as view_name,
           view_definition
    FROM information_schema.views
    WHERE table_schema not in ('information_schema', 'pg_catalog')
    ORDER by schema_name,
             view_name;

    Note

    This script will provide an output of the views created under a particular schema and the SQL script for the view.

  3. We can now select directly from the finance.customer_vw view, just like with any another database object, like so:
    SELECT * from finance.customer_vw limit 5;

    Note

    Here, the finance.customer_vw view abstracts the date_of_birth personally identifiable information (PII) from the underlying table and provides the user an abstracted view of only the essential data for that year to determine the age group.

    This is the expected output:

    outputcustomer_number,first_name,last_name,year_of_birth 
    1 foo bar 1980
    2 john smith 1990
    3 spock spock 1970
    4 scotty scotty 1975
    5 seven of nine 1990 
  4. To delete the previously created view, you can use the following command:
    DROP VIEW finance.customer_vw ;
You have been reading a chapter from
Amazon Redshift Cookbook
Published in: Jul 2021
Publisher: Packt
ISBN-13: 9781800569683
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