Follow recommendations from Amazon Redshift Advisor - Amazon Redshift
How Advisor WorksOptimizing Data Sharing ArchitecturesAmazon Redshift Regions where Advisor is supported

Amazon Redshift will no longer support the creation of new Python UDFs starting November 1, 2025. If you would like to use Python UDFs, create the UDFs prior to that date. Existing Python UDFs will continue to function as normal. For more information, see the blog post .

Follow recommendations from Amazon Redshift Advisor

To help you improve the performance and decrease the operating costs for your Amazon Redshift cluster, Amazon Redshift Advisor offers you specific recommendations about changes to make. Advisor develops its customized recommendations by analyzing performance and usage metrics for your cluster. These tailored recommendations relate to operations and cluster settings. To help you prioritize your optimizations, Advisor ranks recommendations by order of impact.

How Advisor Works

Advisor bases its recommendations on observations regarding performance statistics or operations data. Advisor develops observations by running tests on your clusters/workgroups to determine if a test value is within a specified range. If the test result is outside of that range, Advisor generates an observation for your cluster. At the same time, Advisor creates a recommendation about how to bring the observed value back into the best-practice range.

For multi cluster architectures using Amazon Redshift Data Sharing, Advisor now provides enhanced optimization by analyzing workload patterns across all clusters/workgroups in your data mesh, including clusters/workgroups across different regions. When you share tables between producer and consumer clusters/workgroups, Advisor automatically collects query patterns from all consumer endpoints in the data mesh, unless they are explicitly denylisted, and combines them with producer workloads to generate more effective recommendations. This means your table optimizations—including sort keys, distribution keys, and compression are based on how your data is actually being used across your entire organization, not just on a single cluster. Advisor also supports Amazon Redshift Serverless, automatically maintaining optimization continuity across pause and resume cycles.

For example, suppose that your data warehouse contains tables with suboptimal distribution keys that cause data skew across compute nodes. In this case, Advisor automatically recommends redistributing tables using the DISTKEY parameter to specify a column that evenly distributes data. In another example, suppose that Advisor observes that your cluster has tables without sort keys or with inefficient sort key definitions that result in poor query performance. In this case, Advisor automatically provides recommendations for appropriate sort key columns based on your query patterns to improve data filtering and reduce disk I/O.

Optimizing Data Sharing Architectures

When you use Amazon Redshift Data Sharing to distribute workloads across multiple clusters/workgroups, Advisor helps you optimize performance across your entire data mesh. Advisor automatically analyzes how shared tables are being queried across all consumer clusters/workgroups. This includes understanding which columns are frequently filtered, which tables are commonly joined together, and how data is being scanned. By considering the complete picture of data usage, Advisor generates recommendations that improve performance for all users of your shared data.

By optimizing tables based on usage patterns across your entire organization rather than a single cluster, you can:

  • Make data-driven optimization decisions based on data access patterns across all clusters/workgroups in the mesh

  • Lower storage costs through more effective compression strategies

  • Improve resource utilization across your data mesh

Amazon Redshift Regions where Advisor is supported

The Amazon Redshift Advisor feature is available only in the following AWS Regions:

  • US East (N. Virginia) Region (us-east-1)

  • US East (Ohio) Region (us-east-2)

  • US West (N. California) Region (us-west-1)

  • US West (Oregon) Region (us-west-2)

  • Africa (Cape Town) Region (af-south-1)

  • Asia Pacific (Hong Kong) Region (ap-east-1)

  • Asia Pacific (Hyderabad) Region (ap-south-2)

  • Asia Pacific (Jakarta) Region (ap-southeast-3)

  • Asia Pacific (Melbourne) Region (ap-southeast-4)

  • Asia Pacific (Malaysia) Region (ap-southeast-5)

  • Asia Pacific (Mumbai) Region (ap-south-1)

  • Asia Pacific (Osaka) Region (ap-northeast-3)

  • Asia Pacific (Seoul) Region (ap-northeast-2)

  • Asia Pacific (Singapore) Region (ap-southeast-1)

  • Asia Pacific (Sydney) Region (ap-southeast-2)

  • Asia Pacific (Tokyo) Region (ap-northeast-1)

  • Canada (Central) Region (ca-central-1)

  • Canada West (Calgary) Region (ca-west-1)

  • China (Beijing) Region (cn-north-1)

  • China (Ningxia) Region (cn-northwest-1)

  • Europe (Frankfurt) Region (eu-central-1)

  • Europe (Ireland) Region (eu-west-1)

  • Europe (London) Region (eu-west-2)

  • Europe (Milan) Region (eu-south-1)

  • Europe (Paris) Region (eu-west-3)

  • Europe (Spain) Region (eu-south-2)

  • Europe (Stockholm) Region (eu-north-1)

  • Europe (Zurich) Region (eu-central-2)

  • Israel (Tel Aviv) Region (il-central-1)

  • Middle East (Bahrain) Region (me-south-1)

  • Middle East (UAE) Region (me-central-1)

  • South America (São Paulo) Region (sa-east-1)

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