From dremio
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.
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Provides dbt patterns for building staging, intermediate, and marts data models, incremental materializations, schema tests, and transformation pipelines in analytics engineering.