Analyzing Individual Run Details
Investigate specific schedule executions to diagnose performance, troubleshoot issues, and verify data processing results.
Run Details Overview
Analyze your run execution through different visualization tools:
DAGs View
Visualize the execution flow of scheduled commands through a directed acyclic graph (DAG), showing:
Command dependencies - Understand which models must complete before others can start
Execution order - Track the sequence of operations to optimize pipeline flow
Process relationships - Identify critical paths and potential parallelization opportunities
Model Timeline
Track the temporal sequence of model execution within your run:
Individual model execution times - Spot which models are taking longer than expected
Parallel processing visualization - See which models run simultaneously to maximize efficiency
Performance bottleneck identification - Find slow-running models that delay your entire pipeline
Logs and Artifacts
Explore additional metadata for each run ID, including a breakdown of run logs, source freshness, and artifacts.
Run Logs
The Run Logs section provides a detailed breakdown of each scheduled run, offering insights into execution status, performance, and any encountered issues. This section is divided into three tabs: Summary, Console Logs, and Debug Logs.
Overview Displays high-level execution details for commands, showing the overall completion status, duration, and success metrics.
Warnings and Errors Lists any warnings or errors encountered during the run, such as deprecated configurations or unused paths.
Suggested Actions Recommendations to address identified warnings and errors, including updates for alignment with best practices.
Use Case: Quickly assess if the run was successful, spot any configuration issues, and take corrective actions.

Each tab gives a targeted view of run details, offering a complete understanding of pipeline performance.
Source Freshness
When your schedule includes the dbt source freshness
command, you can:
Monitor when each source table was last updated
Track if data freshness meets your defined SLAs
Identify stale or outdated data sources

Artifacts
The Artifacts section provides access to files that dbt generates after each run. These files help you analyze and troubleshoot your workflows:
SQL Files
Run SQL - View the actual SQL statements executed during the run
Compiled SQL - Examine the optimized SQL used in your data warehouse
Execution Metadata
manifest.json
- Shows project structure (models, sources, and tests)catalog.json
- Contains schema information and column detailsrun_results.json
- Provides execution outcomes of dbt commandssources.json
- Tracks source table metadata and freshness history

Last updated
Was this helpful?