turbovault4dbt 0.2.2

CLI for Turbovault 4 DBT

TurboVault4dbt_cli

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What is TurboVault4dbt_cli?

TurboVault4dbt_cli is an open-source CLI tool that automatically generates dbt models according to datavault4dbt templates. It uses a metadata input of your Data Vault 2.0 from one of the supported databases and creates ready-to-process dbt-models.

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Prerequisites

• Python 3.8+
• Metadata analysis done and stored in a supported format (see below)
• dbt project
• datavault4dbt dbt package

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Supported Metadata Sources

• Snowflake
• BigQuery
• Google Sheets
• Excel
• SQLite DB Files

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How does my metadata needs to look like?

Your metadata needs to be stored in the following tables/worksheets:

• Source Data
• Standard Hubs
• Standard Links
• Non-Historized Links
• Standard Satellites
• Non-Historized Satellites
• Multi-Active Satellites
• Point-In-Time Tables
• Reference Tables

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Installation (CLI Version)

1. Clone this repository:

   git clone https://github.com/ScalefreeCOM/turbovault4dbt.git
   cd turbovault4dbt
   

2. (Recommended) Create and activate a virtual environment:

   python3 -m venv turbovault-env
   source turbovault-env/bin/activate
   

3. Install in editable mode:

   pip install -e .
   

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Quickstart: Using the CLI

1. Prepare your metadata

• See metadata_ddl/ for DDL scripts and Excel templates.
• Configure your metadata connection in src/turbovault4dbt/backend/config/config.ini.

2. Run TurboVault4dbt_cli

Basic usage:

# List available nodes from your metadata Excel file
$ turbovault list --file path/to/your.xlsx

# Generate dbt models for selected nodes
$ turbovault run --file path/to/your.xlsx -s hub1

# Use selectors (e.g., +node, node+, @node)
$ turbovault run --file path/to/your.xlsx -s '+sat1 hub2+ @masat3'

# Specify output directory
$ turbovault run --file path/to/your.xlsx --output-dir my_output_dir -s hub1

Command reference:

• turbovault run --file <input.xlsx> [-s <selector>] [--output-dir <dir>]
  Generate dbt models for selected nodes.
• turbovault list --file <input.xlsx> [-s <selector>]
  List resolved nodes for a selector (dry run).

Selector syntax:

• A+ — node A and all descendants
• +A — node A and all ancestors
• @A — node A, all ancestors, and all descendants
• Multiple selectors can be space-separated

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Regression Testing

To run the regression test suite:

pip install -r requirements-test.txt
pytest tests/test_regression.py

• Add new test cases by creating folders in tests/ with input.xlsx and expected_output/.
• Negative test cases (expected failures) are also supported.

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Project Structure

• src/turbovault4dbt/ — all source code
• tests/ — regression test cases
• pyproject.toml, requirements.txt, etc. — project config in root

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Releases

See GitHub Releases

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License

See LICENSE

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• Open an issue
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