Snowflake

Time-travel support

Snowflake supports time-travel, which means that querying the past is possible and queries stay consistent if the data in Snowflake is updated.
When creating new epochs for the Database, DirectQuery automatically snapshots the current timestamp and use it.

Querying past snapshots is done by using the AT TIMESTAMP SQL syntax. As such, when the historical data becomes unavailable, this is reflected in DirectQuery and queries against those old versions will fail.

Vector supports

Only multi-rows vectors and multi-columns vectors are supported by Snowflake.

The array type available in Snowflake does not offer powerful aggregation functions to work with them.

Gotchas

Nullable fields

Snowflake can define fields with nullable types. DirectQuery is capable of detecting this and defines its internal model accordingly.
While this is not a problem in itself, it can conflict with the rule in Atoti cubes that all levels must be based on non-nullable values. This does not create an issue of any sort as the local model is updated behind the scene, assigning a default value based on the type.
It has a side effect on the query performance, as DirectQuery must convert on-the-fly null values to their assigned default values, as well as adding extra conditions during joins to handle null values on both sides.