Reference
Python packages
cube_dbt

cube_dbt package

cube_dbt package simplifies defining the data model in the semantic layer on top of dbt (opens in a new tab) models. It provides convenient tools for loading the metadata of a dbt project, inspecting dbt models, and rendering them as cubes in YAML.

Installation

Cube Cloud

Add the cube_dbt package to the requirements.txt file in the root directory of your Cube project. Cube Cloud will install the dependencies automatically.

Reference

Dbt class

Encapsulates tools for working with the metadata of a dbt project.

Dbt.__init__

The constructor accepts the metadata of a dbt project as a dict with the contents of a manifest.json file (opens in a new tab).

import json
from cube_dbt import Dbt
 
manifest_path = './manifest.json'
 
with open(manifest_path, 'r') as file:
  manifest = json.loads(file.read())
  dbt = Dbt(manifest)

Use in cases when Dbt.from_file and Dbt.from_url aren't applicable, e.g., when manifest.json is loaded from a private AWS S3 bucket.

Dbt.from_file

This static method loads the metadata of a dbt project from a manifest.json file by its path and returns an instance of the Dbt class.

from cube_dbt import Dbt
 
manifest_path = './manifest.json'
 
dbt = Dbt.from_file(manifest_path)

Dbt.from_url

This static method loads the metadata of a dbt project from a manifest.json file by its URL and returns an instance of the Dbt class.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)

Dbt.filter

This method filters loaded dbt models by their path prefixes, tags, or names.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url).filter(
  paths=['marts/'],  # Only models under the 'marts/' path
  tags=['cube'],     # Only models with the 'cube' tag
  names=['orders']   # Only the 'orders' model 
)

Use to expose only necessary dbt models to the semantic layer.

Note that values in paths should not be prefixed with models/.

Dbt.models

This property exposes a list of loaded dbt models as instances of the Model class.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
 
for model in dbt.models:
  print(model)

Only dbt models that comply with Dbt.filter rules and are not materialized as ephemeral (opens in a new tab) will be returned.

Dbt.model

This method returns a loaded dbt model by its name as an instance of the Model class.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
 
model = dbt.model('orders')
print(model)

Only dbt models that comply with Dbt.filter rules and are not materialized as ephemeral (opens in a new tab) will be returned.

Model class

Encapsulates tools for working with the metadata of a dbt model.

Model.name

This property exposes the name of a dbt model.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
print(model.name)
# For example, 'orders'

Model.description

This property exposes the description of a dbt model.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
print(model.description)
# For example, 'All Jaffle Shop orders'

Model.sql_table

This property exposes the fully-qualified SQL relation name of a dbt model that can be used as the sql_table parameter of a cube.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
print(model.sql_table)
# For example, '"db"."public"."orders"'

Model.columns

This property exposes a list of columns that belong to this dbt model as instances of the Column class.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
for column in model.columns:
  print(column)

Model.column

This method exposes a column that belongs to this dbt model by its name as an instance of the Column class.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
column = model.column('status')
print(column)

Model.primary_key

This method returns the primary key column, if this dbt model has any, as an instance of the Column class. Returns None if there's no primary key in this dbt model.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
print(model.primary_key)

See Column.primary_key for details on the detection of primary key columns.

Model.as_cube

This method renders this dbt model as a YAML snippet that can be inserted into YAML data models. Includes name, description (if present), and sql_table.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
print(model.as_cube())

In the returned multiline string, all lines except for the first one are left-padded with 4 spaces for easier use in YAML data models:

# Jinja template
cubes:
  - {{ model.as_cube() }}
 
# YAML
cubes:
  - name: orders
    description: All Jaffle Shop orders
    sql_table: '"db"."public"."orders"'

Model.as_dimensions

This method renders the list of columns that belong to this dbt model as a YAML snippet that can be inserted into YAML data models.

Optionally, accepts a list of column names that should be ignored in skip.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
 
print(model.as_dimensions(skip=['status']))

See Column.as_dimension for details on the dimension rendering.

In the returned multiline string, all lines except for the first one are left-padded with 6 spaces for easier use in YAML data models:

# Jinja template
cubes:
  - {{ model.as_cube() }}
 
    dimensions:
      {{ model.as_dimensions() }}
 
# YAML
cubes:
  - name: orders
    description: All Jaffle Shop orders
    sql_table: '"db"."public"."orders"'
 
    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

Column class

Encapsulates tools for working with the metadata of a column that belongs to a dbt model.

Column.name

This property exposes the name of a column.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
column = model.column('status')
 
print(column.name)
# For example, 'status'

Column.description

This property exposes the description of a column.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
column = model.column('status')
 
print(column.description)
# For example, 'Order execution status: new, in progress, delivered'

Column.sql

This property exposes the name of a column that can be used as the sql parameter of a dimension.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
column = model.column('status')
 
print(column.sql)
# For example, 'status'

Column.type

This property exposes the data type of a column that can be used as the type parameter of a dimension.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
column = model.column('status')
 
print(column.type)
# For example, 'string'

cube_dbt package applies a set of heuristics to map database-specific types to dimension types. You can check the source code (opens in a new tab) for implementation details.

If a column type is not defined in the metadata of a dbt project, string is used by default.

Column.meta

This property exposes the meta data of a column as a dict that can be used as the meta parameter of a dimension.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
column = model.column('status')
 
print(column.meta)
# For example, '{some: "data"}'

Column.primary_key

This property exposes a bool value that indicates if a column is a primary key or not.

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
column = model.column('status')
 
print(column.primary_key)
# For example, 'False'

By convention, the column is considered a primary key if it has the primary_key tag in the metadata of a dbt project.

Column.as_dimension

This method renders this column as a YAML snippet that can be inserted into YAML data models. Includes name, description (if present), sql, type, primary_key (if True), and meta (if present).

from cube_dbt import Dbt
 
manifest_url = 'https://bucket.s3.amazonaws.com/manifest.json'
 
dbt = Dbt.from_url(manifest_url)
model = dbt.model('orders')
column = model.column('status')
 
print(column.as_dimension())

In the returned multiline string, all lines except for the first one are left-padded with 8 spaces for easier use in YAML data models:

# Jinja template
cubes:
  - {{ model.as_cube() }}
 
    dimensions:
      {% for column in model.columns() %}
      - {{ column.as_dimension() }}
      {% endfor %}
 
# YAML
cubes:
  - name: orders
    description: All Jaffle Shop orders
    sql_table: '"db"."public"."orders"'
 
    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true
 
      - name: status
        description: 'Order execution status: new, in progress, delivered'
        sql: status
        type: string
        meta:
          some: data
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