This repository provides simple examples of using dbt Python models with BigQuery in BigFrames mode.

It includes basic configurations and sample models to help you get started quickly in a typical dbt project.

• profiles.yml: configures your connection to BigQuery.
• dbt_project.yml: configures your dbt project - dbt_sample_project.
• dbt_bigframes_code_sample_1.py: An example to read BigQuery data and perform basic transformation.
• dbt_bigframes_code_sample_2.py: An example to build an incremental model that leverages BigFrames UDF capabilities.
• prepare_table.py: An ML example to consolidate various data sources into a single, unified table for later usage.
• prediction.py: An ML example to train models and then generate predictions using the prepared table.

Before using this project, ensure you have:

• A Google Cloud account
• A dbt Cloud account (if using dbt Cloud)
• Python and SQL basics
• Familiarity with dbt concepts and structure

For more, see:

• https://docs.getdbt.com/guides/dbt-python-bigframes
• https://cloud.google.com/bigquery/docs/dataframes-dbt

Follow these steps to run the Python models using dbt Core.

1. Install the dbt BigQuery adapter:

   pip install dbt-bigquery

2. Initialize a dbt project (if not already done):

   dbt init

   Follow the prompts to complete setup.

3. Finish the configuration and add sample code:

   • Edit ~/.dbt/profiles.yml to finish the configuration.
   • Replace or add code samples in .../models/example.

4. Run your dbt models:

   To run all models:

   dbt run

   Or run a specific model:

   dbt run --select your_model_name