Materialization
Live Query can temporarily store intermediate results so expensive operations don't have to rerun each time you interact with downstream tools. This process is called materialization.
During workflow design, Live Query reuses materialized results when you interact with downstream tools. Materialization is available for selected tools in BigQuery and Snowflake, but what is materialized and when the full input is processed differ between the 2 cloud data warehouses.
Compare Materialization
BigQuery | Snowflake | |
|---|---|---|
What is materialized | A sampled AI-tool preview | A result that requires full-input processing |
Why | Reuse an AI preview during design | Avoid repeatedly performing an operation for which a preview limit offers no benefit |
When full input is processed | When the workflow runs | Before the affected preview can be produced |
Tool coverage | Selected AI tools | Selected transformation tools and modes |
Workflow Refresh
Workflow Refresh refreshes the workflow's source data and marks materialized preview results as outdated. It does not immediately reprocess those results. The next time Live Query needs a preview, it recalculates the affected results using the refreshed source data.