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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

Loading data from remote hosts

The local datasets in a processing server can be loaded into an Amazon Redshift table using the COPY command and the ssh parameter. You can specify the command that Amazon Redshift can execute on the remote server, which will write to standard output. The COPY command will use this to load the data into the table in parallel.

In this recipe, we will learn how to connect to remote hosts to load the data present on the remote host in the part table.

Getting ready

To complete this recipe, you will need to do the following:

  • Gain access to the AWS Console.
  • Deploy an Amazon Redshift cluster in AWS region eu-west-1.
  • Create Amazon Redshift cluster master user credentials.
  • Gain access to any SQL interface, such as a SQL client or the Amazon Redshift Query Editor.
  • Gain access to an Amazon EC2 Linux instance or any Unix or Linux server. You will need open connectivity between Amazon EC2 Linux or your local Linux/Unix server to Amazon...
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