Snowflake DBML Generator

The Snowflake DBML Generator is an open-source tool that translates Snowflake database schemas into DBML (Database Markup Language), facilitating easy generation of ER diagrams and comprehensive documentation of database structures.

For data engineers, analysts, and ML engineers, it enables you to bootstrap a rich interactive DB diagram directly from Snowflake.

Quick Start

pip install snowflake-dbml-generator
snowflake-dbml --interactive

Examples

Using Snowflake's Sample Data Sets, here are demonstrations of generating DBML and visualizing it with dbdiagram.io:

1. Snowflake TPCH SF100 Dataset

2. Snowflake TPCDS SF100TCL Dataset

Features

Automatic DBML Generation: Converts Snowflake schema definitions into DBML automatically.
Complex Schema Support: Manages complex relationships, including composite foreign keys.
Interactive Mode: Guides through configuration file generation and database connection setup.
Customizable Visualization: Allows customization of DBML file appearances with configurable color schemes.
SQL Comments Extraction: Extracts SQL table and column comments as DBML notes.
Table Statistics: Includes table statistics like row count and size in bytes in the DBML output.

Installation

Prerequisites

Python 3.6+
Snowflake account credentials

Install from PyPI

pip install snowflake-dbml-generator

Build from Source

git clone https://github.com/ryanrozich/snowflake-dbml-generator.git
cd snowflake-dbml-generator
pip install -e .

Usage

Command Line Interface

Run the generator via the command line. For detailed usage, see Configuration Parameters.

snowflake-dbml --user <username> --password <password> --account <account_id> --warehouse <warehouse> --database <database> --role <role>

Interactive Mode

For guided setup:

snowflake-dbml --interactive

Configuration Parameters

Configuration can be set via environment variables, a .env file, or command-line arguments. Learn more about setting configuration parameters.

Configuration File

For advanced setups, particularly when Snowflake does not enforce foreign key relationships, a config.json file can define how relationships are inferred. See Configuring Relationships for detailed setup.

Generating DBML

To generate a DBML file after setting up your configuration:

snowflake-dbml --config-file config.json > output.dbml

Contributing

Contributions are welcome! Whether it's tweaking code, enhancing documentation, or reporting bugs, we'd love to see your pull requests. Let's make this tool even better together!

Here’s a refined section for your README.md to document all special commit message keywords, including instructions for controlling version increments. This section can be integrated into your GitHub repository documentation:

Commit Message Keywords

In our continuous integration process, commit message keywords play a crucial role in determining the nature of changes and the corresponding actions in our deployment pipeline. Below are the keywords that you can use to control versioning and deployment:

Keywords:

[skip version]
- Purpose: Prevents any version bump or tag creation. Use this keyword for commits that make non-functional changes, such as updates to documentation, that do not warrant a new version number.
- Example:
```
git commit -m "Update README [skip version]"
```
MAJOR:
- Purpose: Triggers a major version bump, indicating backward-incompatible changes that require this level of escalation.
- Example:
```
git commit -m "MAJOR: Redesign database schema for scalability"
```
MINOR:
- Purpose: Triggers a minor version bump, suitable for backward-compatible functionality enhancements.
- Example:
```
git commit -m "MINOR: Add new analytics features"
```
PATCH: (default if no keyword is specified)
- Purpose: Applies a patch version bump, typically for bug fixes and minor changes that don’t add new features or break existing ones.
- Example:
```
git commit -m "PATCH: Fix data loading issue"
```

Workflow Integration:

These keywords are evaluated in our GitHub Actions workflow to decide the type of version bump or to skip versioning entirely. It ensures that our versioning reflects the nature of changes accurately, maintaining consistency and predictability in our release process.

By using these keywords appropriately in your commit messages, you help maintain the integrity of the versioning process and ensure that each release is meaningful.

ryanrozich/snowflake-dbml-generator