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Data Trust Engineering

Data Trust Engineering

Vendor-neutral, engineering-first patterns for trusted, AI-ready data systems.

This organization hosts open work around the Data Trust Engineering Manifesto: practical patterns, tools, and community conversation—not a product catalog or sales funnel.

Start here
Website datatrustmanifesto.org
Main repository DataTrustEngineering — manifesto, patterns, Trust Dashboard MVP
Slack Join the community
Discussions GitHub Discussions

What’s in the main repo

  • Manifesto — principles for certifying data systems by use case, risk, and value
  • Patterns — DataOps-style practices (quality, contracts, lineage, observability, AI evals, …)
  • Trust Dashboard MVP — working artifact for trust / fairness / drift style monitoring
  • Site — Hugo site published at datatrustmanifesto.org

How to engage

  1. Read the manifesto and quick start
  2. Star or watch DataTrustEngineering
  3. Join Slack or open a Discussion / issue
  4. Contribute patterns or tools — see CONTRIBUTING.md

Open engineering conversation. Not demos-for-hire.

Related: InfoLibrarian Corporation — Brian Brewer’s company & portfolio (classic product EOS); open work continues here under Data Trust Engineering.