dbt Semantic layer cells
dbt Semantic layer cells allow users of all technical levels to pull in data backed by consistent, governed metrics in their Semantic layer, and be confident in their results.
- Users will need Can Edit permissions to create and reference dbt Semantic layer cells.
To use a dbt Semantic layer cell, first ensure:
- You have enabled the dbt Server integration as outlined here.
- You are using dbt v1.6 or higher. (Legacy versions will only be supported until dbt deprecates them.)
Configure dbt Semantic layer cells
First, add a dbt Semantic layer cell to your project (dbt Semantic layer cells are grouped with the other Data type cells). Then, select a metric of interest via the +Add button on the left of the cell. Multiple metrics can be added, if desired.
From here, you can specify the time grain and start/end dates for the returned data. You can also specify additional dimensions which are compatible with the selected metric(s). Optionally, you can configure secondary calculations like a period-over-period calculation or running total. Once you have configured the dbt Semantic layer cell, execute the cell to retrieve the results of the query.
Use dbt Semantic layer results in downstream cells
dbt Semantic layer cells return a pandas DataFrame with a default naming scheme of
metric_result_n. This DataFrame can be used in downstream cells anywhere a pandas DataFrame can be used. For example, dbt Semantic layer cell results can be: