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)

Managing data distribution

Distribution style is a table property that dictates how that table's data is distributed throughout the compute nodes. The goal of data distribution is to leverage the massively parallel processing of Amazon Redshift and reduce the I/O during query processing to improve performance. Amazon Redshift Advisor provides actionable recommendations on distribution style for the table via the alter statement. Using automatic table optimization allows you to self-manage the table distribution style based on workload patterns:

  • KEY: The value is hashed. The same value goes to the same location (slice).
  • ALL: The entirety of the table data goes to the first slice of every compute node.
  • EVEN: Round robin data distribution is performed across the compute nodes and slices.
  • AUTO: Combines the EVEN, ALL, and KEY distributions:

Figure 7.6 – Data distribution styles

In this recipe, you will learn how Amazon Redshift...