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https://github.com/rittmananalytics/ra_dbt_to_dataform

An open-source tool that partially automates the migration of dbt packages to Dataform
https://github.com/rittmananalytics/ra_dbt_to_dataform

bigquery dataform dbt dbt-core migration-tool

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An open-source tool that partially automates the migration of dbt packages to Dataform

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# dbt to Dataform Migration Tool



This tool partially automates the process of converting dbt (data build tool) projects to Dataform, focusing on BigQuery as the data warehouse. It not a turn-key tool that handles all aspects of the migration for you, but it will take care of the simple (and some more complex) tasks so that you can concentrate on the more complex parts.



It also optionally leverages OpenAI's API for complex conversions and syntax checking; to make use of this feature you will need an OpenAI API key.



# Features



- Converts dbt models to Dataform SQLX files, with limitations as detailed below

- Translates dbt source definitions to Dataform declarations

- Converts dbt macros to Dataform functions using GPT-4 (requires OpenAI API key)

- Preserves project structure, adapting it to Dataform best practices

- Handles (with limitations) dbt-specific Jinja syntax and converts it to JavaScript

- Supports conversion of dbt variables to Dataform project config variables

- Automatically converts common dbt_utils functions to their BigQuery equivalents

- Uses GPT-4 to check and correct Dataform syntax in converted files (requires OpenAI API key)

- Generates a detailed conversion report highlighting potential issues and syntax corrections



## How does it work?



The migration tool employs a combination of rule-based transformations for standard conversions and AI-powered processing for more complex scenarios. This hybrid approach enables the tool to handle both straightforward translations and nuanced, context-dependent conversions effectively.



While the process is largely automated, it is designed to complement rather than replace human expertise. The tool provides a solid foundation for migration, but user intervention may be necessary for project-specific optimizations and handling of unsupported features.



The migration process comprises seven steps:



1. **Project Analysis**: 

   - The RepositoryAnalyzer scans the dbt project structure.

   - It identifies models, tests, macros, and YAML files.



2. **Project Configuration Conversion**:

   - The ProjectConfigConverter translates dbt_project.yml to dataform.json.

   - It handles project-wide settings and variables.



3. **Source Conversion**:

   - The SourceConverter processes dbt source definitions.

   - It creates individual SQLX files for each source table in the Dataform project.



4. **Model Conversion**:

   - The ModelConverter translates each dbt SQL model to a Dataform SQLX file.

   - It handles reference conversions, variable replacements, and macro translations.



5. **Macro Conversion**:

   - The MacroConverter transforms dbt macros into Dataform JavaScript functions.

   - Macros are converted using the OpenAI API, for manual review, correction and completion



6. **Syntax Checking and Correction**:

   - The SyntaxChecker uses the OpenAI API to verify and correct Dataform syntax in converted files.



7. **Report Generation**:

   - The ConversionReport creates a detailed report of the conversion process.

   - It highlights potential issues, syntax corrections, and areas needing manual review.



## Automatically Converted dbt_utils Functions



The following dbt_utils functions are automatically converted to their BigQuery equivalents:



1. `{{ dbt_utils.type_string() }}` -> `STRING`

2. `{{ dbt_utils.type_int() }}` -> `INT64`

3. `{{ dbt_utils.type_numeric() }}` -> `NUMERIC`

4. `{{ dbt_utils.type_timestamp() }}` -> `TIMESTAMP`

5. `{{ dbt_utils.star(from=ref('model_name')) }}` -> `*`

6. `{{ dbt_utils.surrogate_key(['col1','col2']) }}` -> `TO_HEX(MD5(CONCAT(CAST(col1 AS STRING), CAST(col2 AS STRING))))`

7. `{{ dbt_utils.datediff(...) }}` -> `DATE_DIFF(...)`

8. `{{ dbt_utils.dateadd(...) }}` -> `DATE_ADD(...)`

9. `{{ dbt_utils.date_trunc(...) }}` -> `DATE_TRUNC(...)`

10. `{{ dbt_utils.date_part(...) }}` -> `EXTRACT(...)`



## Use of OpenAI API



1. **dbt Jinja Macro Conversions**:

   - The `MacroConverter` class uses GPT-4, via the OpenAI API, to convert dbt Jinja macros to Dataform JavaScript functions.

   - It sends the dbt macro code to the API and receives a converted JavaScript function.

   - Prompt example:

     ```

     Convert the following dbt macro to a JavaScript function for Dataform:



     {dbt_macro_content}



     Follow these guidelines:

     1. Convert Python/Jinja syntax to JavaScript.

     2. Replace dbt-specific functions with Dataform equivalents where possible.

     3. For SQL generation, use JavaScript template literals.

     4. If there's no direct Dataform equivalent for a dbt function, implement the functionality in JavaScript.

     5. Add any necessary comments or explanations.



     Provide only the converted JavaScript function:

     ```

   - The API returns a JavaScript function that can be used in Dataform.



2. **Syntax Checking and Correction**:

   - The `SyntaxChecker` class uses GPT-4, via the OpenAI API, to verify and correct the syntax of converted Dataform files.

   - It sends the converted SQLX content to the API, which checks for Dataform-specific syntax issues and suggests corrections.

   - Prompt example:

     ```

     Check if the following Dataform SQLX code is valid. If it's not valid, correct it and explain the changes made.

     If it's valid, just respond with "Valid".



     Always include the full corrected code in your response, even if only small changes were made.

     Wrap the corrected code in ```sqlx and ``` tags.



     Code:

     {sqlx_content}

     ```

   - The API returns either "Valid" or a corrected version of the SQLX code with explanations.



# Setup



1. Clone the repository:

```

git clone https://github.com/yourusername/dbt-to-dataform.git

cd dbt-to-dataform

```



2. Create and activate a virtual environment (optional but recommended):

```

python -m venv venv

source venv/bin/activate  # On Windows use venv\Scripts\activate

```

3. Install the required packages:

```

pip install -r requirements.txt

```



# Usage



```bash

python main.py   --openai-api-key 

```

: Path to the local dbt repository

: Path to output the Dataform project

--openai-api-key: Optional. Your OpenAI API key for complex conversions and syntax checking

--verbose: Optional. Enable verbose output



## Post-Conversion Steps



After running the converter:



- Review the conversion_report.json and conversion_summary.txt files

- Address any issues highlighted in the conversion report

- Review and test all converted models, especially those flagged in the report

- Implement any custom logic that couldn't be automatically converted

- Update any remaining dbt-specific syntax or functions that weren't automatically handled



Limitations



- Complex dbt macros may require manual adjustment after conversion

- Custom dbt tests might need additional implementation in Dataform

- The tool assumes a BigQuery setup; adjustments may be needed for other warehouses

- Certain dbt-specific features might not have direct equivalents in Dataform

- While this converter handles many aspects of dbt projects, some features are not currently supported or require manual intervention:



1. **Seeds**: The converter does not automatically handle dbt seed files. These CSV files need to be manually imported into your data warehouse and declared in Dataform.



2. **dbt Semantic Layer**: Dataform does not have an equivalent to dbt's semantic layer. Metric definitions and semantic models will need to be reimplemented using Dataform's capabilities.



3. **Snapshots**: While the converter attempts to translate dbt snapshots, Dataform's approach to slowly changing dimensions (SCDs) differs from dbt's. Manual adjustment may be necessary.



4. **Custom Tests**: dbt's custom tests don't have a direct equivalent in Dataform. These will need to be reimplemented using Dataform's assertion capabilities.



5. **Packages**: dbt packages are not automatically converted. You'll need to find Dataform equivalents or reimplement the functionality.



6. **Documentation**: dbt's documentation generation is not directly translated. Dataform has its own documentation features that will need to be set up manually.



7. **Exposures**: Dataform doesn't have a direct equivalent to dbt's exposures. This information will need to be managed outside of Dataform.



8. **Advanced Hooks**: While basic pre- and post-hooks can be converted, advanced hook usage in dbt might require manual implementation in Dataform.



Always review the conversion report and test thoroughly after conversion to ensure all critical functionality is preserved.



Contributing

Contributions to improve the converter are welcome. Please submit pull requests with clear descriptions of the changes and their purposes.

License

MIT License