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DBT

To integrate your predictions into your DBT workflow, use the dbt-mindsdb adapter:

Adapter for Documentation Install from PyPi
MindsDB (dbt-mindsdb) Profile Setup pip install dbt-mindsdb

Usage

Initialization

  1. Create dbt project:
dbt init [project_name]
  1. Configure your profiles.yml, currently MindsDB only supports user/password authentication, as shown below on: ~/.dbt/profiles.yml
mindsdb:
  outputs:
    dev:
      type: mindsdb
      database: 'mindsdb'
      host: '127.0.0.1'
      port: 47335
      schema: 'mindsdb'
      password: ''
      username: 'mindsdb'
  target: dev

mindsdb:
  outputs:
    dev:
      type: mindsdb
      database: 'mindsdb'
      host: 'cloud.mindsdb.com'
      port: 47335
      schema: '[dbt schema]'
      username: '[mindsdb cloud username]'
      password: '[mindsdb cloud password]'
  target: dev
Key Required Description Example
type ✔️ The specific adapter to use mindsdb
host ✔️ The MindsDB (hostname) to connect to cloud.mindsdb.com
port ✔️ The port to use 3306 or 47335
schema ✔️ Specify the schema (database) to build models into The MindsDB datasource
username ✔️ The username to use to connect to the server mindsdb or mindsdb cloud user
password ✔️ The password to use for authenticating to the server pass

Create predictor

Create table_name.sql (table_name will be used as the name of the predictor):

Parameter Required Description Example
materialized ✔️ Always predictor predictor
integration ✔️ Name of integration to get data from and save result to. It must be created in MindsDB beforehand. photorep
predict ✔️ Field to be predicted name
predict_alias Alias for predicted field predicted_name
using Configuration options for trained model ... 
{{
    config(
        materialized='predictor',
        integration='photorep',
        predict='name',
        predict_alias='predicted_name',
        using={
            'encoders.location.module': 'CategoricalAutoEncoder',
            'encoders.rental_price.module': 'NumericEncoder'
        }
    )
}}
    SELECT *
    FROM stores;

Create predictions table

Create table_name.sql (If you need to specify schema, you can do it with a dot separator: schema_name.table_name.sql):

Parameter Required Description Example
materialized ✔️ Always table table
predictor_name ✔️ Name of predictor model from Create predictor store_predictor
integration ✔️ Name of integration to get data from and save result to. It must be created in MindsDB beforehand. photorep 
{{
    config(
        materialized='table',
        predictor_name='store_predictor',
        integration='photorep'
    )
}}
    SELECT a, bc
    FROM ddd
    WHERE name > latest;

Note that each time dbt is run, the results table will be rewritten.

Testing

  1. Install dev requirements
pip install -r dev_requirements.txt
  1. Run pytest
python -m pytest tests/