Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Most enterprise data engineering teams + AI/ML platform teams + data governance teams + AI agent builders + analytics engineers using the modern data stack today have been either (a) hand-rolling per-source metadata ingestion (Snowflake + BigQuery + Databricks + dbt + Airflow + Looker each with their own custom metadata pipeline -- high maintenance burden, no unified metadata model, no real-time m
Who should use it
Who should skip it
Skip DataHub: Apache-2.0 Context Platform for Data and AI Stack (Metadata Lake + Data Catalog + Data Observability + Data Governance + ML + AI Agent Discovery, 12,226*, LinkedIn Origin) if the source link, documentation, or setup requirements do not align with your current workflow or stack.
About this signal
DataHub: Apache-2.0 Context Platform for Data and AI Stack (Metadata Lake + Data Catalog + Data Observability + Data Governance + ML + AI Agent Discovery, 12,226*, LinkedIn Origin) is tracked by RepoRadar as a tool in the Radar section. It was first seen on 2026-07-08 and last updated on AUTOFILL_NOW. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. DataHub: Apache-2.0 Context Platform for Data and AI Stack (Metadata Lake + Data Catalog + Data Observability + Data Governance + ML + AI Agent Discovery, 12,226*, LinkedIn Origin) leads on workflow potential (9.1) and practical usefulness (9.0); its lowest signal is setup ease (6.4), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned DataHub: Apache-2.0 Context Platform for Data and AI Stack (Metadata Lake + Data Catalog + Data Observability + Data Governance + ML + AI Agent Discovery, 12,226*, LinkedIn Origin) a composite score of 8.4 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 0.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
The 12; 226* repo is at active maintenance but the consumer SHOULD note the default branch is `master`; not `main` (the LICENSE lives at `master/LICENSE`); the consumer SHOULD note the production deployment requires a Kafka + Elasticsearch + Neo4j + MySQL/Postgres + React UI stack -- the consumer SHOULD review the stack configuration before deploying.