Operations
The Operations tab manages the semantic layer of a Custom Model. The semantic layer is a structured representation built from the connected data sources that enables the AI to understand and query your data intelligently.
Operations require Admin or Builder role.
Semantic Layer Operations
Create Semantic Model
Builds the semantic layer for the first time based on the connected data sources and their configured filters. Run this operation after:
- All data sources are connected in the Data Sources tab
- Custom Model filters are configured in the Filters tab
The operation analyzes all connected data sources, generates cross-source semantic metadata (tables, columns, hierarchies, descriptions), and makes the model ready for querying.
Update Semantic Model
Refreshes the semantic layer to reflect changes. Run this operation when:
- Data source schemas have changed (new columns, renamed tables, added tables)
- Custom Model filters were modified in the Filters tab
- A new data source was connected or disconnected
Scheduling updates: If your underlying data sources change frequently, create a Job Definition in the Operations area to schedule regular updates. Navigate to the link shown after enabling the update operation to configure the schedule and cron interval. The right frequency depends on your data sources — some schemas change daily, others remain stable for months.
For static models where the semantic layer is expected to remain unchanged, schedule updates only when schema changes occur. Locking the model after validation helps protect against accidental modifications and signals to users that the model is stable and production-ready.
Delete Semantic Model
Removes the semantic layer entirely. After deletion, the model cannot answer data queries until the semantic layer is rebuilt with Create Semantic Model.
Deleting the semantic model removes the foundation that all charts and dashboards built on this model rely on. Charts and dashboards will lose their data connection and become non-functional.
Only delete the semantic model if you intend to fully reset and rebuild the model's configuration. In production environments, avoid deletion unless there is no alternative.
If you need to update the semantic layer, use Update Semantic Model instead.
Recommended Workflow
For a new Custom Model:
- Connect data sources (Data Sources tab)
- Configure Custom Model filters (Filters tab)
- Run Create Semantic Model — builds the initial semantic layer
- Review the generated metadata (Descriptions, Hierarchies tabs)
- Test the model with representative queries before sharing
- Lock the model to protect the configuration
For an existing model after schema changes:
- Update data source filters if needed (Filters tab on the data source)
- Update Custom Model filters if needed (Filters tab on the model)
- Run Update Semantic Model
- Re-validate representative queries
Deciding on Update Frequency
There is no universal update interval. Consider your situation:
| Scenario | Recommendation |
|---|---|
| Schema changes rarely (stable production DB) | Manual update only, triggered when changes occur |
| Schema changes regularly (active development) | Schedule weekly or after each deployment |
| Schema changes never (static CSV/file source) | No updates needed; lock the model |
| Multiple data sources with different change rates | Schedule by the most frequently changing source |
Best Practices
- Always run Create Semantic Model after the initial data source and filter setup — do not share the model before the semantic layer exists.
- Use Update Semantic Model instead of Delete + Create whenever possible to preserve any manually enriched descriptions and hierarchies.
- Lock the model after validating a stable production configuration to prevent accidental changes.
- If update jobs are scheduled via Job Definitions, monitor the Job Runs area regularly to detect failures early.