Enhanced job run granularity for Databricks (beta)

CloudZero now provides run-level visibility for your Databricks workloads, enabling you to track and optimize individual job executions rather than being limited to job-level analysis. You can now view, filter, and group your Databricks spend by:

• run_name: the specific name of each job execution
• job_run_id: the unique identifier for each job run

These attributes capture point-in-time execution data, giving you the granularity needed to measure the impact of configuration changes and optimizations across multiple runs of the same job.