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Amazon Redshift Cookbook

Amazon Redshift Cookbook

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

Amazon Redshift Cookbook

4.8 (9)
By: Shruti Worlikar, Arumugam, 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

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 ;
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