![]() ![]() The marketing data mart cluster demonstrates manual WLM.The enterprise data warehouse demonstrates Auto WLM with query priorities.For this post, we use the following examples: Each data warehouse has different workloads, SLAs, and concurrency requirements.Ī database administrator (DBA) will implement appropriate WLM strategies on each Redshift data warehouse based on their use case. Solution overviewĪs we discussed in the previous section, ExampleCorp has multiple Redshift data warehouses: one enterprise data warehouse and two downstream Redshift data warehouses. Auditor data mart – This is only used for a few hours a day to run scheduled reports.ĮxampleCorp would like to better manage their workloads using WLM.The cluster admin understands the exact resource requirements by workload type. Marketing data mart – This has predictable extract, transform, and load (ETL) and business intelligence (BI) workloads at specific times of day.The enterprise standardized data from the EDW cluster is accessed by multiple consumer clusters using the Redshift data sharing feature to run downstream reports, dashboards, and other analytics workloads. Enterprise data warehouse (EDW) platform – This has all write workloads, along with some of the applications running reads via the Redshift Data API.For this post, we focus on the following: The following figure illustrates the user personas and access in ExampleCorp.ĮxampleCorp has multiple Redshift clusters. For this multitenant architecture by department, ExampleCorp can achieve read/write isolation using the Amazon Redshift data sharing feature and meet its unpredictable compute scaling requirements using concurrency scaling. ExampleCorp would like to manage resources and priorities on Amazon Redshift using WLM queues. The service-level performance requirements vary by the nature of the workload and user personas accessing the datasets. They have variety of workloads with users from various departments and personas. Use case overviewĮxampleCorp is an enterprise using Amazon Redshift to modernize its data platform and analytics. We also show how to assign user roles to WLM queues and how to use WLM query insights to optimize configuration. We guide you through common WLM patterns and how they can be associated with your data warehouse configurations. This post provides examples of analytics workloads for an enterprise, and shares common challenges and ways to mitigate those challenges using WLM. We have introduced support for Redshift roles in WLM queues, you will now find User roles along with User groups and Query groups as query routing mechanism. You can use RBAC to control end-user access to data at a broad or granular level based on their job role. Role-based access control (RBAC) is a new enhancement that helps you simplify the management of security privileges in Amazon Redshift. When users belonging to a user group or role run queries in the database, their queries are routed to a queue as depicted in the following flowchart. WLM queues are configured based on Redshift user groups, user roles, or query groups. In Amazon Redshift, you implement WLM to define the number of query queues that are available and how queries are routed to those queues for processing. Each workload type has different resource needs and different service-level agreements (SLAs).Īmazon Redshift workload management (WLM) helps you maximize query throughput and get consistent performance for the most demanding analytics workloads by optimally using the resources of your existing data warehouse. We also see more and more data science and machine learning (ML) workloads. ![]() With Amazon Redshift, you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs.
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