MindsDB and DBT

DBT is a development framework that facilitates data transformation processes. You can read more about DBT here.

​

Usage

​

Installing Adapter for DBT

MindsDB provides an adapter to integrate your predictions into the DBT workflow.

To find out more about the dbt-mindsdb adapter, follow the instructions here.

You can install the dbt-mindsdb adapter by executing this command: pip install dbt-mindsdb.

​

Profile Setup

1. Create a DBT project:

   dbt init [project_name]
   

2. Configure your profiles.yml file:

   What MindsDB Supports” Currently, MindsDB supports only user/password authentication. Please see below ~/.dbt/profiles.yml file for details.

   Self-Hosted Local Deployment

   MindsDB Cloud

       mindsdb:
         outputs:
         dev:
           type: mindsdb
           database: "mindsdb"
           host: "127.0.0.1"
           port: 47335
           schema: "mindsdb"
           username: "mindsdb"
           password: ""
         target: dev
   

Let’s list the required data fields.

​

Creating a Predictor

Create a table_name.sql file where table_name is the name of the predictor.

{{
    config(
        materialized='predictor',
        integration='photorep',
        predict='name',
        predict_alias='predicted_name',
        using={
            'encoders.location.module': 'CategoricalAutoEncoder',
            'encoders.rental_price.module': 'NumericEncoder'
        }
    )
}}
    SELECT *
    FROM stores;

Let’s list the required and optional data fields.

​

Creating Predictions Table

Create a table_name.sql file where table_name is used as the name of the predictor. Or you can create a schema_name.table_name.sql file where schema_name is the name of the integration and table_name is the name of the predictor.

{{
    config(
        materialized='table',
        predictor_name='store_predictor',
        integration='photorep'
    )
}}
    SELECT a, bc
    FROM ddd
    WHERE name > latest;

Let’s list the required data fields.

​

Testing

1. Install dev requirements:

   pip install -r dev_requirements.txt
   

2. Run tests:

   python -m pytest tests/
   

​

What’s Next?

Now that you are all set, we recommend you check out our Tutorials and Community Tutorials sections, where you’ll find various examples of regression, classification, and time series predictions with MindsDB.

To learn more about MindsDB itself, follow the guide on MindsDB database structure. Also, don’t miss out on the remaining pages from the SQL API section, as they explain a common SQL syntax with examples.

Have fun!