model2data 0.1.1

Generate analytics-ready datasets from DBML models

model2data

model2data turns data models into analytics-ready datasets in seconds.

Given a DBML file, it generates synthetic but realistic data, a complete dbt project scaffold, and everything you need to start analyzing or testing data pipelines.

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What problem does it solve?

Building analytics or testing dbt pipelines often requires realistic data, but using real data raises privacy concerns, and creating mock data manually is time-consuming. model2data automates this by generating synthetic datasets from your data model definitions, ensuring privacy-safe, deterministic, and relationship-preserving data for development and testing.

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How it works (high level)

1. Parse DBML: Reads your database schema from a DBML file, extracting tables, columns, types, and relationships.
2. Generate Data: Uses Faker and custom logic to create realistic synthetic data, respecting foreign keys and constraints.
3. Scaffold dbt Project: Creates a dbt project with seeds (CSV files), staging models, profiles, and tests, ready to run with DuckDB.

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Installation

pip install model2data

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Quick start

We provide an example Hacker News dataset in examples/hackernews.dbml.

Generate a project with synthetic data:

model2data generate --file examples/hackernews.dbml --rows 200 --seed 42

This creates a dbt_hackernews/ folder with your data and dbt setup.

Run dbt to load and transform the data:

cd dbt_hackernews
dbt deps
dbt seed
dbt run

Your analytics-ready dataset is now in DuckDB!

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Generated dbt project structure

The generated dbt project includes:

dbt_{project_name}/
├── seeds/
│   └── {project_name}/
│       ├── table1.csv
│       └── table2.csv
├── models/
│   └── {project_name}/
│       └── staging/
│           ├── __sources.yml
│           ├── stg_table1.sql
│           ├── stg_table1.yml
│           └── ...
├── macros/
│   └── generate_schema_name.sql
├── dbt_project.yml
├── profiles.yml  # DuckDB config
└── {project_name}.duckdb

• Seeds: CSV files with generated synthetic data.
• Staging Models: Basic dbt models that load from seeds.
• Sources & Tests: YAML configs defining sources and basic tests (not_null, unique).
• Profiles: Pre-configured for DuckDB with schema handling.

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Design decisions / non-goals

• DuckDB Default: Chosen for its zero-config, file-based nature, making it easy to get started without database setup. Other adapters can be configured manually.
• dbt Integration: Leverages dbt's transformation capabilities for a familiar workflow in analytics engineering.
• Synthetic Data: Uses deterministic generation for reproducibility; not intended for production use or as a replacement for real data.
• Non-goals: This is not a data migration tool, ETL pipeline, or real-time data generator. It focuses on static, synthetic datasets for testing and prototyping.

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Limitations

• Supports basic DBML features; complex constraints or advanced SQL types may not be fully handled.
• Synthetic data generation is heuristic-based and may not perfectly mimic real-world distributions or edge cases.
• Currently optimized for DuckDB; other databases require manual profile adjustments.
• No support for incremental models or advanced dbt features in generated projects.

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Roadmap

• Support for additional database adapters (e.g., Snowflake, BigQuery).
• Enhanced data type handling and custom generators.
• Integration with more dbt features like incremental models.
• Web-based DBML editor and data preview.

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Contributing

We welcome contributions!

• Open issues for bugs or feature requests.
• Submit PRs to add new DBML examples, custom data generators, or improvements.
• Ensure all new features include tests if possible.

See CONTRIBUTING.md for detailed guidelines.

Code of Conduct

Please read our Code of Conduct to understand our community standards.

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License

MIT License. See LICENSE for details.