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Answer: Apply appropriate tags when creating a cluster for each job across different business units.
To accurately monitor costs and attribute Databricks usage to specific business units and teams (e.g., for chargebacks), tagging clusters and pools is recommended. These tags are reflected in detailed DBU usage reports and can be used for AWS EC2 and EBS instance cost analysis. Databricks automatically applies default tags to all pools and clusters, with additional default tags for job clusters and resources used by Databricks SQL. Custom tags can be added to objects managed by Databricks, and tag propagation varies depending on whether a cluster is created from a pool. For clusters not created from a pool, tags propagate to EC2 instances as expected. Both cluster and pool tags appear in DBU usage reports, with pool tags taking precedence over cluster tags in case of conflicts. Therefore, the most effective solution is to use appropriate tags when creating clusters for jobs across different business units.
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A data engineering manager is looking for an effective way to monitor and track the costs associated with Databricks usage across various business units within the organization. With numerous jobs running on daily, weekly, and monthly schedules, tracking the cost of each individual job run proves challenging. Which of the following solutions would best address this need?
A
Investigate the logs for each job to determine the run time and approximate the usage cost based on cluster pricing.
B
Utilize a single cluster for all jobs across different business units to simplify cost tracking.
C
Request the workspace admin to monitor DBU usage for various jobs across business units and compile an analysis sheet.
D
Establish separate workspaces for each business unit, containing their respective jobs, to facilitate easier cost tracking.
E
Apply appropriate tags when creating a cluster for each job across different business units.
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