A Single place to Discover, Collaborate, and Get your data right

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

An elegant, opinionated framework for deploying BrightHive Data Resources with zero coding.

Databricks-native data trust pipeline — intake certification, drift gating, and control benchmarking in a single deployable product.

Collection of Data Science Projects for Workshops and Speaking Engagements.

A Databricks control pattern that certifies every record before downstream consumption. 7 contract checks, replay detection, schema drift handling, and quarantine with explicit reasons. 56 passing tests. Databricks Free Edition validated. Enterprise Data Trust, Chapter 1.

A reproducible benchmark that scores data controls against known failure scenarios with precision, recall, and ground truth. Custom approach achieved perfect recall; industry baselines missed injected drift. 37 passing tests, 10/10 gates. Enterprise Data Trust, Chapter 3.

Curated data quality and trust patterns focused on ensuring reliability, consistency, and confidence in analytics and decision-making systems.

A data retrieval system that tracks data provenance and transformations

A release control that detects when business columns collapse despite healthy schema and row counts. Distribution stability scoring, 6 publication gates, and blocked Gold refresh when the health score dropped from 1.0 to 0.20. 50 passing tests. Databricks Free Edition validated. Enterprise Data Trust, Chapter 2.