Deprecated Feature: This feature is being phased out.
dbt can run on lakeFS with a Spark adapter or Presto/Trino adapter.
Both Spark and Presto use Hive metastore or Glue to manage tables and views.
When creating a branch in lakeFS, you receive a logical copy of the data that can be accessed by
To run a dbt project on a newly created branch, you need to have a copy of the metadata as well.
The lakectl dbt command generates all the metadata needed in order to work on the newly created branch, continuing from the last state in the source branch. The dbt lakectl command does this using dbt commands and lakectl metastore commands.
To run the lakectl-dbt commands you need to configure both dbt and lakectl.
Assuming dbt is already configured, using either a Spark or Presto/Trino target
you’ll need to add configurations to give lakeFS access to your catalog (metastore).
This is done by adding the following configurations to the lakectl configuration file (by default
profile: default # optional, implies using a credentials file
lakectl copies all the models materialized as tables and incremental directly on your metastore. However, copying views should be done manually or with lakectl.
generate_schema_name macro could be used by lakectl to create models using dbt on a dynamic schema.
The following command will add a macro to your project, allowing lakectl to run dbt on the destination schema using an environment variable.
lakectl dbt generate-schema-macro
If you don’t want to add the
generate_schema_name macro to your project,
you can create the views on the destination schema manually.
For every run:
- use the
- change the default schema to be the branch schema in your dbt configuration file,
- run dbt on all views.
dbt run --select config.materialized:view
Creating the schema From your dbt project run:
lakectl dbt create-branch-schema --branch my-branch --to-schema my_branch
You can find more advanced options here.