Data Trust Engineering (DTE)

🎉 Data Trust Engineering - Soft Launch!

We're live! Try our working trust dashboard and join the community.

Get Started • Join Discussion • Quick Start Guide

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Data Trust Engineering (DTE) is a vendor-neutral, engineering-first approach to building trusted, AI-ready data systems.
This repo hosts the Manifesto, Patterns, and the Trust Dashboard MVP.

• 🌐 Site: https://datatrustmanifesto.org/
• 📜 Manifesto: Manifesto.md
• 🧭 Patterns and Samples: docs/patterns/README.md
• 📊 Trust Dashboard(s): tools/data-trust-dashboard

Follow & Join

• ⭐ Star the repo to show support
• 👀 Watch (Releases only or All activity)
• 🍴 Fork to experiment
• 💬 Discuss in GitHub Discussions or join our Slack
• 📦 Try it: see QUICK_START.md
• 🙌 Contribute: read CONTRIBUTING.md

Data Trust Engineering (DTE) is a community-driven approach that empowers data teams to build trusted, AI-ready systems through practical engineering patterns and open-source collaboration. Born from the Data Trust Engineering Manifesto, DTE provides actionable frameworks for certifying data systems by use case, risk, and value, blending DataOps principles with hands-on implementation. Our first artifact, the DTE Trust Dashboard, demonstrates real-time data and AI trust monitoring—more tools are coming from our community! Join us at datatrustmanifesto.org and fuel the #DataTrustCommunity.

Data Trust Engineering Data and Application Moderniztion: From data sources through AI assurance with community-driven patterns

Why DTE?

Traditional data governance often struggles with complexity, conflating compliance requirements with technical data management and creating barriers for engineering teams. Organizations face challenges when AI models encounter data quality issues or when scaling data trust across cloud environments. DTE addresses these practical challenges through:

• Certification: Practical validation patterns for datasets by use case, risk, and business value, supporting compliance goals indirectly.
• Engineering Rigor: Hands-on tools and patterns using Great Expectations, Apache Atlas, and other open-source frameworks.
• AI-Readiness: Proven approaches for fairness monitoring, drift detection, and model governance.
• Cloud-Native: Patterns that scale across data mesh architectures and hybrid cloud environments.
• Community-Driven: Open-source evolution through GitHub contributions and collaborative problem-solving.

DTE serves organizations of all sizes—from enterprises building regulatory reporting systems to SMBs seeking practical data trust solutions to data teams driving innovation with AI.

Our First Artifact: DTE Trust Dashboard

The DTE Trust Dashboard demonstrates DTE principles in action, providing interactive visualization of AI trust metrics built with Chart.js and Plotly:

• AI Fairness: Demographic parity monitoring across protected attributes (integrates with Fairlearn).
• Data Drift: Real-time drift tracking and alerts (compatible with Evidently AI).
• Model Performance: Accuracy, F1 score, and AUC-ROC monitoring (integrates with MLflow).
• Guardrails: Multi-dimensional view of trust principles and safety metrics.

Find it in /tools/data-trust-dashboard/. This working dashboard serves as a foundation for community contributions and demonstrates how DTE patterns can be implemented in practice.

Explore USE_CASES.md to see how DTE principles translate into actionable code examples.

Getting Started

Quick Start

1. Clone the Repo:

   git clone https://github.com/datatrustengineering/DataTrustEngineering.git
   cd DataTrustEngineering

Live Demo

• Trust Dashboard: Try it live
• Full Site: datatrustmanifesto.org

2. Explore the Trust Dashboard:

   • Navigate to /tools/data-trust-dashboard
   • Run the interactive Streamlit version: streamlit run app.py
   • View the static HTML version in any browser
   • Customize with your own data sources and metrics

3. Read the Manifesto:

   • See /Manifesto.md for DTE's core principles and philosophy.

4. Integrate Tools:

   • Data Quality: Great Expectations, Soda Core for validation
   • Lineage: Apache Atlas, OpenLineage for metadata tracking
   • AI Governance: Fairlearn, SHAP, MLflow for model monitoring

Repository Structure

• /Manifesto.md: DTE's core philosophy and principles
• /tools: Working artifacts and code examples
• /docs: Patterns, guides, and practical implementations
• /community: Contribution guidelines and governance
• /LICENSE.md: MIT License for open collaboration

Contributing

Help build the DTE ecosystem! Our community welcomes contributions that advance practical data trust engineering:

• Artifacts: Data quality tools, lineage trackers, or AI monitoring scripts in /tools
• Patterns: Documentation of proven approaches in /docs/patterns
• Case Studies: Real-world implementations and lessons learned
• Code Examples: Working implementations that others can fork and adapt

Start with "good first issues" or join our community discussions. See CONTRIBUTING.md and CODE_OF_CONDUCT.md for guidelines.

Roadmap

• Q4 2025: Expand data quality patterns; add lineage tracking examples
• Q1 2026: Community case studies and real-world implementations
• Q2 2026: Enhanced documentation and contributor resources

Why It Matters

DTE provides practical value through:

• Proven Patterns: Community-tested approaches for common data trust challenges
• Engineering Focus: Code-first solutions that integrate into existing workflows
• Open Collaboration: Shared knowledge and tools that benefit the entire ecosystem
• Measurable Impact: Demonstrated improvements in data quality and AI reliability

License

MIT License - encouraging open collaboration and reuse.

Acknowledgments

Authors: Brian Brewer | Founder | https://www.infolibcorp.com

#DataTrustCommunity