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.

Collection of Data Science Projects for Workshops and Speaking Engagements.

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

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

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.

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 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.