Book Image

Amazon Redshift Cookbook

By : Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel
Book Image

Amazon Redshift Cookbook

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)

Exporting a data lake from Amazon Redshift

Amazon Redshift empowers a lake house architecture, allowing you to query data within the data warehouse and data lake using Amazon Redshift Spectrum and also to export your data back to the data lake on Amazon S3, to be used by other analytical and ML services. You can store data in open file formats in your Amazon S3 data lake when performing the data lake export to integrate with your existing data lake formats.

Getting ready

To complete this recipe, you will need the following to be set up:

  • An IAM user with access to Amazon Redshift
  • An Amazon Redshift cluster deployed in the eu-west-1 AWS Region with the retail dataset created from Chapter 3, Loading and Unloading Data, using the Loading data from Amazon S3 using COPY recipe
  • Amazon Redshift cluster masteruser credentials
  • Access to any SQL interface such as a SQL client or the Amazon Redshift Query Editor
  • An AWS account number—we will refer to this in...