dbt-depp 0.3.1

DBT Enexis Python Postgres adapter - Run python scripts from any dbt project.

DEPP - DBT Python Postgres Adapter

This package support for running python models in dbt for postgres directly within your dbt project Inspired on dbt-fal but made to be both extremely high performance and fully typed Also supports polars dataframe besides pandas and more are coming soon

Features

• Run Python scripts as dbt models - Write Python logic directly in your dbt project
• Fully typed Python models - Complete type safety with IDE support (see typing docs)
• Multiple DataFrame libraries - Support for both pandas and Polars dataframes (more comming soon)
• Auto-generated documentation - Python docstrings automatically become model descriptions in dbt docs
• High performance - Blazing fast execution using connectorx and asyncpg
• PostgreSQL integration - Seamless integration with PostgreSQL databases

Installation

Install using uv (recommended):

uv add dbt-debb

Or using pip:

pip install dbt-depp

Quick Start

1. Add to your profiles.yml: Make sure to both add a db_profile with all your details and add your database and schema

your_project:
  target: dev
  outputs:
    dev:
      type: depp
      db_profile: dev_postgres
      database: example_db
      schema: test
      
    dev_postgres:
      type: postgres
      host: localhost
      user: postgres
      password: postgres
      port: 5432
      database: example_db
      schema: test
      threads: 1

2. Create Python models in your dbt project:

# models/customer_analysis.py
"""Analyze customer purchase patterns using Polars."""
import polars as pl
from dbt.adapters.depp.typing import PolarsDbt, SessionObject

def model(dbt: PolarsDbt, session: SessionObject) -> pl.DataFrame:
    # Reference existing models with full type safety
    customers_df = dbt.ref("customers")
    orders_df = dbt.ref("orders")

    # Perform analysis using Polars
    result = (
        customers_df
        .join(orders_df, on="customer_id", how="inner")
        .group_by("customer_region")
        .agg([
            pl.col("order_amount").sum().alias("total_revenue"),
            pl.col("customer_id").n_unique().alias("unique_customers"),
            pl.col("order_amount").mean().alias("avg_order_value")
        ])
        .sort("total_revenue", descending=True)
    )

    return result

3. dbt run!

Development

This project uses uv for dependency management:

# Install dependencies
uv sync

# Run tests
uv run pytest

# Build package
uv build

Documentation

• Getting Started Guide - Complete setup guide including profiles.yml configuration and first Python models
• Type Safety Guide - Complete guide to using DEPP's type system for better IDE support and code safety

Requirements

• Python >= 3.12
• dbt-core >= 1.10.0
• PostgreSQL database

License

This project is open source and available under the MIT License.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.