dbx-sql-runner 0.2.2

A lightweight SQL transformation tool for Databricks SQL

dbx-sql-runner

A lightweight, library-first SQL transformation tool for Databricks SQL, inspired by DBT.

📘 Full Documentation: https://munish7771.github.io/dbx-sql-runner/

Features

• Simple SQL Models: Just write .sql files. No complex boilerplate.
• Automated Dependency Management: Reference other models using {upstream_model} and let the runner build the DAG for you.
• Environment Aware: Seamlessly switch between Dev and Prod using profiles.yml and Environment Variables.
• Library Design: Import dbx_sql_runner in your Python scripts (great for Airflow/Databricks Jobs) or run it via CLI.
• Flexible Sources: Define external tables in profiles.yml and reference them as {source_name} in your SQL.
• Automated Linting: Built-in linter (using Ruff) to ensure code quality.
• Alerting: Send notifications to a webhook URL on run completion or failure.

Installation

Development

To install the project in editable mode:

pip install -e .

To run docs site:

npm run start

Running Tests

To run the automated test suite:

pip install .[dev]
python -m pytest

Production

To install the package normally:

pip install dbx-sql-runner

Configuration (profiles.yml)

Create a profiles.yml file to store your credentials. Do not commit this file to version control.

server_hostname: "dbc-xxxxxxxx-xxxx.cloud.databricks.com"
http_path: "/sql/1.0/warehouses/xxxxxxxxxxxxxxxx"
access_token: "${DBX_ACCESS_TOKEN}"  # Env var expansion supported for any field
catalog: "my_catalog"
schema: "my_schema"
sources:
    # keys here can be used in SQL as {my_source}
    my_source: "prod_catalog.schema.table"
    raw_sales: "raw_data.sales_table"

Usage

1. CLI (Easiest)

Run your project from the command line. By default, it looks for profiles.yml in the current directory.

# Initialize a new project
dbx-sql-runner init my_project

# Run with default profile (profiles.yml)
dbx-sql-runner run

# Run with custom profile
dbx-sql-runner run --profile my_config.yml

# Preview execution plan
dbx-sql-runner build

2. Python (Advanced)

For fine-grained control (e.g., inside a Databricks Job):

from dbx_sql_runner.api import run_project

# Run models in the 'models/' directory using the config from 'profiles.yml'
run_project(models_dir="models", config_path="profiles.yml")

Project Structure

.
├── models/                  # SQL files (.sql)
│   └── example.sql
├── dbx_sql_runner/          # Library source code
│   ├── adapters/            # Database Adapters
│   ├── api.py               # Public API
│   ├── cli.py               # Command Line Interface
│   ├── exceptions.py        # Custom Exceptions
│   ├── linter.py            # Linting Logic
│   ├── models.py            # Data Models
│   ├── project.py           # Model Loading & DAG
│   ├── runner.py            # Execution Orchestrator
│   └── scaffold.py          # Project Scaffolding
├── profiles.yml             # Configuration (gitignored)
├── pyproject.toml           # Project metadata
└── README.md

Defining Models

Create .sql files in your models/ directory.

• Use header comments for metadata.
• Use {upstream_model} syntax for references (automatically infers dependency).
• Use {source_name} to reference sources defined in profiles.yml.

-- name: my_first_model
-- materialized: table
-- partition_by: date

/*
    Welcome to your first dbx-sql-runner model!
    
    This is where you define your SQL logic.
    You can refer to other models like this: {upstream_model_name}
    Or refer to sources defined in profiles.yml like this: {my_source}
*/

SELECT 
    1 as id, 
    current_date() as date, 
    'Hello World' as message