What does the run_result artifact track during dbt execution?

The run_result artifact is vital in tracking the timing and status information for executed nodes during a dbt run. When a dbt job is executed, the run_result captures detailed metrics about how long each node took to complete and whether it successfully ran or encountered any issues. This information is crucial for understanding the performance of the dbt models, diagnosing bottlenecks, and ensuring that the transformations are executed as intended.

By gathering timing data, the run_result can help analytics engineers identify which parts of the data pipeline may need optimization. Moreover, monitoring the status of each node alerts users to any failures or successes, allowing for quicker troubleshooting and ensuring data quality.

The other options, while related to various aspects of data management, do not accurately encompass what the run_result artifact specifically tracks during a dbt execution. Errors and warnings are part of a broader narrative but are not the sole focus of the run_result. Data lineage and transformation history relate to understanding data flow and origin, which is different from the execution timing and status. Permissions and access controls concern data governance and security, which falls outside the scope of what the run_result artifacts are designed to track.