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Working with dbt-dremio for data transformations on Dremio Cloud. Use when the user asks about dbt, data modeling, dbt profiles, dbt run, or data transformations with dbt.
How this skill is triggered — by the user, by Claude, or both
Slash command
/dremio:dremio-dbtThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
```yaml
# profiles.yml
my_project:
target: dev
outputs:
dev:
type: dremio
threads: 4
cloud_host: api.dremio.cloud # NOT software_host
cloud_project_id: "{{ env_var('DREMIO_PROJECT_ID') }}"
use_ssl: true
user: [email protected] # required for Cloud
pat: "{{ env_var('DREMIO_PAT') }}"
dremio_space: MyNamespace # database = namespace
dremio_space_folder: no_schema # schema = omitted (root)
object_storage_source: MyNamespace # where tables materialize
object_storage_path: no_schema
dbt-dremio maps dremio_space → database, dremio_space_folder → schema. The sentinel no_schema omits the schema component. There is no equivalent sentinel for the database position — no_schema, no_database, and "" all emit as literal strings. The namespace must always go in dremio_space (database).
In sources.yml, use schema: no_schema to avoid doubling. If dremio_space: Medicaid, do NOT set schema: Medicaid — that produces "Medicaid"."Medicaid"."table".
Requires Python <=3.13 (3.14 breaks dbt-dremio). Use a venv:
cd <dbt-project-dir>
source .env # sets DREMIO_PAT and DREMIO_PROJECT_ID
source .venv/bin/activate
dbt compile # check SQL in target/compiled/
dbt run # materialize models
dbt test # run data tests
Model naming: stg_ (staging views), int_ (intermediate views), mart_ (materialized tables). All flat in the namespace root.
npx claudepluginhub dremio/claude-plugins --plugin dremioBuilds and modifies dbt models with SQL transformations using ref() and source(), creates tests, validates results with dbt show. For dbt projects: modeling, debugging errors, data exploration, testing, change evaluation.
Provides expert guidance for creating, modifying, and optimizing dbt pipelines targeting Google BigQuery. Activates when users work with dbt models, optimize SQL, or set up dbt projects.
Provides dbt patterns for building staging, intermediate, and marts data models, incremental materializations, schema tests, and transformation pipelines in analytics engineering.