Override more than one project in Dataform

[bastian_moraga-]

Hi, to everyone

I am currently working on dataform creating a sql flow to create some tables in my dev environment. The thing is, I am working with two development Projects because each one of them has different audiences and properties. Everything is executing successfully.

The problem starts when I am pushing to production, I need to override these two development projects that I am working on, to the production projects. 

I am new at Dataform but I know that I'd have to create variables or

[ms4446]

Dataform facilitates a structured approach to managing your development, testing, and production environments, enabling seamless transitions and clear separation between different stages of your data pipeline lifecycle:

• Development: Individual or team-based local environments, potentially involving multiple projects to cater to different audiences and setups.

• Staging (Optional): An intermediate environment to simulate production-like conditions for thorough testing before actual deployment.

• Production: The live environment where end-users interact with your data transformations, representing the final output of your development efforts.

Managing Project Overrides

To ensure smooth project transitions from development to production, consider the following strategies:

1. Environment Configuration in dataform.json:

   • Utilize the environments configuration within your dataform.json to define environment-specific settings, including Google Cloud Project IDs for each stage (development, staging, production).

   • This approach allows you to specify different schemas, datasets, and other configurations that vary between environments.

2. Environment-Specific Variables:

   • Define variables for any settings that differ between environments, such as dataset names or table prefixes.

   • Use these variables within your SQL scripts to dynamically reference environment-specific resources.

   Example SQL Script:

   CREATE TABLE `${env["project_id"]}.${variables.target_dataset}.my_table` AS ( -- your SQL transformations );

3. Deployment with Variables:

   • When deploying your Dataform project to production, specify the appropriate environment variables to ensure your scripts target the correct resources.

   Example Deployment Command:

   dataform run --env=production --vars='{"target_dataset":"my_production_dataset"}'

Best Practices

• CI/CD Automation: Automate the deployment process with CI/CD pipelines to minimize manual errors and streamline transitions between environments.

• Thorough Testing: Before deploying to production, thoroughly test your data transformations in a staging environment (if available) to catch and resolve any issues.

• Version Control: Maintain meticulous version control of your Dataform project, including dataform.json and environment-specific configurations, to track changes and manage deployments effectively.

Additional Considerations

• Ensure your approach and tool usage align with the latest Dataform capabilities and best practices as provided by Google Cloud. Dataform's integration and features may evolve, so consulting the latest Dataform documentation is crucial for staying up-to-date.

• The syntax and examples provided are based on Dataform's standard practices. However, always verify against the current documentation or tooling provided by Google Cloud for the most accurate and effective use of Dataform in your projects.

By adhering to these revised strategies and best practices, you can efficiently manage your Dataform projects across multiple environments, ensuring a smooth and error-free transition from development to production.

[bastian_moraga-]

Thank you for your very useful response!

I only have one question left, what would be the case if I want to write to two projects per environment?

Example destinations: Development: default_project_dev & project_1_dev, Production: default_project_prod & project_1_prod

Should I use some IF logic in my .SQLX files?

Could you provide me with an example?

[ms4446]

For optimal data transformation management within a single environment using Dataform, consider the following strategies and important points:

Methods

1. Separate SQLX Files (Primary): Structure your Dataform code with separate SQLX files for transformations targeting distinct projects. Employ variables for dynamic project name management, boosting flexibility and maintainability.

2. Alternative Organization (As Needed): If your data transformations are closely linked across projects, consider organizing your SQLX files around logical data domains instead. While this approach might involve some increased complexity by having transformations target multiple projects within a file, it can potentially streamline the overall understanding of your data flow.

3. Conditional Logic (Sparingly): While IF logic within SQLX files is an option, use it with caution. Prioritize separate SQLX files as a general practice for better maintainability.

Deployment & Execution

• Include relevant SQLX files in project definitions.
• Use environment variables to dynamically control target projects during execution.

Important Considerations

• Permissions: Employ fine-grained IAM permissions for the Dataform service account, enabling control at the dataset level within multiple projects for tighter security.
• Complexity Management: As complexity grows, explore refactoring for long-term maintainability.
• Automated Testing: Implement automated tests on your SQLX files to proactively catch errors across environments early in the development cycle.

Additional Suggestions

• Clear Documentation: Use thorough comments within SQLX files to explain the purpose and target environment of each transformation.
• Robust Version Control: Leverage version control to effectively manage configurations and transformations across environments, track changes, and facilitate collaboration.