Integration of the dbt semantic layer with Metabase

dbt are launching, this October, their "semantic layer".

In short, it'll allow you to define metrics within dbt. Using an example from their GitHub, you'd define a metric within a .yml file like this:

.

This would allow you to constrain a metric by what table it comes from, the aggregation, what time granularities can be used, and what dimensions you can cut it by. You can also do a variety of other things (e.g. specify filters).

You would then effectively create SQL on the fly via:

select * 
from {{ metrics.metric(
    metric_name='average_order_amount',
    grain='week',
    dimensions=[],
) }} 

Some of this can already be achieved in Metabase with metrics & segments - but firstly there are benefits to defining the logic further upstream, and secondly there are more constraints (i.e. how you cut the data).

They mention in their docs that "While dbt does not currently provide a BI experience for exploring these metrics, we’re working on a number of integrations with BI partners that will help unlock the full value of the metrics layer."

Are there any plans to integrate dbt metrics into Metabase?