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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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Scheduling queries using Amazon Redshift Query Editor V2

Amazon Redshift Query Editor V2 (QEV2) allows users to schedule queries on the Redshift data warehouse. Users can schedule long-running or time-sensitive queries, refresh materialized views at regular intervals, and load or unload data.

In this recipe, we will automate the refresh of the customer_agg_mv materialized view, so that the data is up to date when the base tables change.

Getting ready

To complete this recipe, you will need the following:

  • Amazon Redshift data warehouse (serverless or provisioned cluster) endpoint deployed in AWS Region eu-west-1
  • IAM user with access to Amazon Redshift, Amazon Redshift QEV2, and Amazon EventBridge
  • IAM role attached to Amazon Redshift data warehouse that can access Amazon EventBridge; we will reference it in the recipes as [Your-Redshift_Role]
  • We will reuse the customer_agg_mv materialized view that was set up using the Chapter 2 recipe titled...
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Amazon Redshift Cookbook
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