Amazon Redshift workload management helps optimize resource allocation and query prioritization across different workloads like ETL, BI, and ad-hoc queries to meet service-level agreements by configuring separate queues with concurrency and memory settings.

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<p>This article discusses how to effectively manage different types of workloads on Amazon Redshift using Workload Management (WLM). It covers the following key points:</p>
<p>Specifically, the article covers:</p>
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<li>Overview of use cases and workloads for an example enterprise (ExampleCorp)</li>
<li>Choosing between Auto WLM and Manual WLM based on workload predictability</li>
<li>Understanding WLM parameters like service classes, user roles, user groups, and query groups</li>
<li>Setting up Auto WLM with examples for an enterprise data warehouse cluster</li>
<li>Setting up Manual WLM with examples for a marketing data mart cluster</li>
<li>Using Amazon Redshift Serverless for infrequent auditor workloads</li>
<li>Monitoring WLM using system views and query monitoring rules</li>
<li>Conclusion and additional resources</li>
</ul>
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Manage your workloads better using Amazon Redshift Workload Management

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