Skip to content

CONTRIBUTING.md

Latest commit

 

History

History
110 lines (80 loc) · 6.37 KB

CONTRIBUTING.md

File metadata and controls

110 lines (80 loc) · 6.37 KB
title Contributing to Data Trust Engineering (DTE)

Welcome to the Data Trust Engineering (DTE) community! We're building a collaborative ecosystem of practical patterns and tools that help data teams implement trust and reliability in their AI and data systems. Inspired by successful open-source communities, DTE brings together data professionals from diverse backgrounds to share proven approaches for certification, monitoring, and quality assurance. Whether you're enhancing the DTE Trust Dashboard, proposing new artifacts, or sharing case studies, your contributions help advance practical data trust engineering.

This guide explains how to contribute to the DataTrustEngineering repository, ensuring a productive, inclusive community. Join us at datatrustmanifesto.org or our [Slack org]([Slack Invite Link]).

Table of Contents

Code of Conduct

All contributors must follow our CODE_OF_CONDUCT.md, based on the Contributor Covenant. Be respectful, constructive, and focused on DTE's mission to provide practical engineering patterns for data trust. Harassment, off-topic discussions, or disruptive behavior will be addressed by maintainers to keep our community welcoming.

What Can You Contribute?

DTE is a community-driven initiative, and we welcome contributions that align with our Manifesto and advance practical data trust engineering. Examples include:

  • Artifacts: Tools and scripts for data quality (e.g., Great Expectations configurations), lineage tracking (e.g., OpenLineage integrations), or AI governance (e.g., Fairlearn monitoring patterns). See /tools.
  • Patterns and Guides: Document proven approaches and best practices in /docs/patterns.
  • Case Studies: Share real-world implementations and lessons learned in /docs/case-studies. Use our template.
  • Trust Dashboard Enhancements: Add metrics, integrations, or UI improvements to /tools/data-trust-dashboard.
  • Documentation: Improve clarity, add examples, or create tutorials in /docs.
  • Code Examples: Contribute working implementations that others can adapt and extend.

Good First Issues: New to DTE? Start with issues tagged "good first issue" (e.g., documentation improvements, adding examples, or fixing typos). These are great ways to learn and contribute to the community!

How to Contribute

  1. Explore the Repo:

    • Read the README.md and Manifesto.md to understand DTE's approach.
    • Check /tools and /docs for existing artifacts and inspiration.
    • Review USE_CASES.md for practical implementation examples.

  2. Discuss Your Idea:

    • Open a GitHub Issue to propose your contribution (e.g., "New pattern: Data quality validation workflow"). Use our issue templates for clarity.
    • Join our [Slack org]([Slack Invite Link]) (#contributions channel) to discuss ideas with the community.

  3. Fork and Clone:

    git clone https://github.com/[YourUsername]/DataTrustEngineering.git
cd DataTrustEngineering
    • Fork the repo on GitHub to create your own copy.

  4. Make Changes:

    • Create a branch: git checkout -b feature/your-contribution-name.
    • Add your contribution (e.g., a pattern in /docs/patterns, a script in /tools).
    • Commit with a clear message: git commit -m "Add data quality validation pattern".

  5. Push and Submit a PR:

    • Push to your fork: git push origin feature/your-contribution-name.
    • Open a Pull Request (PR) to the main branch, describing your changes and linking to the relevant issue.

Pull Request Process

We review PRs to ensure alignment with DTE's principles and community standards. Follow these steps:

  1. Create a PR:

    • Use a descriptive title (e.g., "Add lineage tracking pattern with OpenLineage").
    • Link to the related GitHub Issue (e.g., "Closes #123").
    • Explain your changes and how they benefit the community.

  2. Review Process:

    • Maintainers will review within 5-7 days, checking for:
      • Alignment with the DTE Manifesto.
      • Code/documentation quality (e.g., markdown linting, functional examples).
      • Relevance to DTE's goals (e.g., practical implementation patterns).
    • Expect constructive feedback (e.g., "Can you add an example for cloud deployments?"). Revise as needed.

  3. Automated Checks:

    • PRs undergo markdown linting (via GitHub Actions) for documentation consistency.
    • Code-based artifacts should include basic validation or example usage.

  4. Approval and Merge:

    • Approved PRs are merged into the main branch.
    • If a PR doesn't align with our focus, we'll provide feedback and suggest alternative approaches.

Community Guidelines

  • Stay On-Topic: Contributions should support DTE's focus on practical data trust engineering. Off-topic ideas may be redirected to more appropriate forums.
  • Be Collaborative: Engage in GitHub Discussions or community channels before submitting large changes to align with shared goals.
  • Respect Feedback: Maintainers may request changes to ensure quality and consistency. Respond constructively to help improve the contribution.
  • Avoid Chaos: Follow the CODE_OF_CONDUCT.md. Disruptive behavior may lead to warnings or temporary blocks.

Recognition

Your contributions strengthen the DTE community! We recognize contributors by:

  • Listing you in CONTRIBUTORS.md with your permission.
  • Highlighting valuable contributions in community channels.
  • Offering opportunities for deeper involvement based on sustained contributions.

Questions or Ideas?

  • GitHub Issues: Open an issue for questions, ideas, or feedback.
  • Community Discussion: Join our Slack channels for real-time conversations.
  • Documentation: Check our patterns and guides for implementation examples.

Thank you for contributing to DTE! Your efforts help build a stronger, more collaborative data trust engineering community.

#DataTrustCommunity