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DataHub that discovers metadata, validates data quality, traces lineage, detects schema changes, and automatically generates fixes, SQL, and documentation for data engineers.
A column gets dropped, a serving model goes quietly wrong, nobody notices for weeks. Tether blocks that PR. When it misses one, it writes the missing lineage edge to DataHub, and stops missing it.
Payne helps data teams know when a dataset is truly safe to shut down, and catches when that changes.
DataGuardian AI is an autonomous data-governance agent that uses DataHub context to detect risks, reason over impact, recommend fixes, and write approved remediation back to the data graph.
Catch the upstream change before the model fails. An agent that traces DataHub's ML lineage to find schema changes that silently break production models - and writes them back as Incidents.
A governed career handbook where DataHub lineage routes evidence-bound challenges into accountable review—without changing canonical content.
Marg maps UK immigration rules as a dependency graph in DataHub, so students see what one changed date breaks downstream. Every rule is cited to gov.uk, and it refuses to guess.
An autonomous AI agent using DataHub MCP Server to trace breaking schema changes, auto-generate fixed dbt SQL & Airflow DAGs, and submit self-healing GitHub PRs with updated metadata lineage.
A DataHub-native agent that turns risky schema changes into validated, auditable migration decisions.
Autonomous AI agent using DataHub MCP & LangGraph to detect stale data, compute blast radius, write back metadata, and auto-generate dbt models & Airflow DAGs to heal data pipelines in real time.
MCPatch uses DataHub context to find which AI agents break after an MCP/API change, repair the right repositories, verify the fixes, and prepare safe draft PRs for human review.
Declare desired state in one file. Controllers read DataHub via its Skills, reason from evidence and past incidents, price changes, then—with your approval—write outcomes back to DataHub.
HTTP 200 can hide a broken business path. This agent uses DataHub schema and lineage to find the risky workflow, prove the route changed, and write verified evidence back to the affected asset.
An agent that takes the 2 a.m. page: it walks DataHub lineage to the root cause, ranks the blast radius, files the incident, and writes a post-mortem back so the next incident resolves faster.
Akashic Weaver watches DataHub for breaking schema changes, traces every downstream dependency, and ships the fix as a pull request — before your team even knows something broke.
5 autonomous AI agents that find governance gaps in live DataHub instances and write verified fixes directly back through MCP, with zero human intervention and live schema validation.
Chronicle Graph gives AI agents durable memory for data incidents: real DataHub context, calibrated confidence, human-confirmed patterns, and approval-gated write-back to DataHub
Aegis is an AI on-call engineer that monitors DataHub 24/7, investigates issues via real lineage and live data, proposes a fix, waits for approval, then applies it and logs the outcome.
janus is an ML deployment gatekeeper that reconstructs whether your training data was healthy at the moment you trained, and writes the evidence back into DataHub as a Model Passport.
An AI agent that audits DataHub for missing docs, generates schemas, lineage context, and PII tags via Gemini, and writes results back so future users inherit full context.
The missing CI for your ML supply chain.
DataShield Guardian is an autonomous AI agent that protects ML systems from data leakage, PII, drift, and schema failures—tracing risks through DataHub lineage and taking action before models break.
Keel:AI-powered trust layer for data pipelines. Scores assets on hygiene, detects incidents, investigates root causes, and routes smart alerts to the right teams -with human approval always required.
Tripwire warns on GitHub PRs before you merge real DataHub lineage, downstream dashboards & owners, then writes pre-merge risk back into the catalog.
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