Enterprise Data Trust
Data reliability you can measure, not assume
Most organizations monitor data quality. Few can answer a direct question: what percentage of failures would your controls actually catch?
Explore the portfolioOur approach
Strategy, controls, and evidence — moving together.
There is no shortcut to data trust. It requires intake certification, failure detection, and measurable proof that your controls work — built on Databricks, validated against known failure scenarios.
01
Certify at the source
Validate every record before downstream consumption. Schema drift, duplicate replays, and contract violations are caught and quarantined — not silently passed.
02
Block before it propagates
Detect when business columns collapse despite healthy schema and row counts. Block Gold publication when distribution stability degrades.
03
Prove the controls work
Benchmark controls against known failures with ground truth scoring. Produce detection rates and evidence bundles — not feature lists.
Portfolio
Five chapters. One question per chapter. Measured evidence for each.
Each chapter addresses a specific gap in enterprise data reliability and provides the measured outcomes that technology leaders require.
Chapter 1
Trusted Source Intake
Every record earns its way into Bronze. A governed intake gate that catches schema drift, blocks duplicate replays, and quarantines invalid records with explicit reasons.
7 contract checks
10 replays blocked
23 rows quarantined
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280+ GitHub clones
Chapter 2
Silent Failure Prevention
Before corrupted numbers reach the board, block the refresh. Distribution-stability scoring across monitored columns, with configurable gates that halt Gold publication the moment health degrades.
6 release gates
Health score 1.00 → 0.20
Gold refresh blocked
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360+ GitHub clones
Chapter 3
Measurable Control Effectiveness
Prove your controls actually work. A dual-track benchmark that injects known failures, runs challenger versus baseline approaches, and produces ground-truth-scored evidence you can show a regulator.
Recall 1.00 vs 0.8767
10/10 gates passing
JSON evidence bundle
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280+ GitHub clones
Chapter 4 · Platform
DriftSentinel
Intake certification, drift gating, and control benchmarking in a single governed pipeline. Chapters 1–3 unified with an operator dashboard for real-time governance visibility.
Unified pipeline
Operator dashboard
Databricks-deployable
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Chapter 5 · Platform
ÆtheriaForge
Every Medallion transformation scored for coherence loss. Entity resolution across source systems, temporal conflict reconciliation, and schema enforcement — with append-only evidence. Published on PyPI.
Coherence scoring
Entity resolution
PyPI published
View on GitHub
Coming Soon
GovForge
The first monetized product in the EthereaLogic portfolio — applying every control pattern from Chapters 1–5 to a domain where data trust is not optional.