18 Best Data Lineage Tools Compared (2026)
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A structured comparison of 18 data lineage tools covering column-level vs table-level lineage, parsed vs declared lineage, and cross-platform coverage. Tools are grouped by category: governance platforms (Decube, Atlan, Alation, Collibra, Informatica), platform-native lineage (Microsoft Purview, Databricks Unity Catalog), lineage specialists (Solidatus, Octopai), open source options (OpenMetadata, DataHub, Amundsen), mid-market catalogs (Secoda, OvalEdge, dbt), and observability tools (Monte Carlo, Metaplane, Acceldata). Key decision criteria include whether column-level lineage is supported per connector, whether lineage is auto-parsed or manually declared, and whether traces survive across platform boundaries. Pricing ranges from free open source to six-figure enterprise agreements. The piece also covers AI governance requirements and Asia-Pacific regulatory context (OJK, APRA, MAS).
Table of contents
Key TakeawaysWhat This Comparison CoversHow We Ranked This List1. Decube2. Atlan3. Alation4. Collibra5. Informatica6. Microsoft Purview7. Databricks Unity Catalog8. Solidatus9. Octopai10. OpenMetadata11. DataHub12. Amundsen13. Secoda14. OvalEdge15. dbt16. Monte Carlo17. Metaplane18. AcceldataData Lineage Tools ComparedTable Level or Column Level: The Question That Decides ItParsed or Declared: Why Lineage Goes StaleDoes the Trace Survive Outside One PlatformWhat Data Lineage Tools CostHow to Choose: Five Decision RulesRegulators That Change the ShortlistLineage Is What Turns an AI Governance Policy Into EvidenceFour Mistakes That Cost the MostFrequently Asked QuestionsQuestions this post answers
What is the difference between table level and column level data lineage and why does it matter?
Table-level lineage shows that a report depends on a table; column-level lineage shows that a specific figure depends on a specific field in that table. Column-level is essential for impact analysis — when a column type change is proposed, table-level lineage flags 40 reports to check manually, while column-level lineage identifies the 3 reports that actually read that field, reducing analyst work from a week to a minute. Teams evaluating lineage tools for audit or impact analysis track this distinction on daily.dev.
Which open source data lineage tools support column level lineage parsed from query logs?
OpenMetadata and DataHub both provide column-level lineage parsed from query logs and are free to use. OpenMetadata is considered the strongest open source option for column-level lineage with a unified metadata model, while DataHub was built at LinkedIn scale with an event-driven architecture and wide connector coverage. Both offer managed commercial versions. Amundsen is lighter and largely table-level only. Engineers choosing between open source lineage platforms find the trade-offs covered on daily.dev.
What are the EU AI Act compliance deadlines for high risk AI systems?
The European Union Digital Omnibus on AI entered into force on 27 July 2026 and adjusted the high-risk obligations. Standalone high-risk systems have until 2 December 2027, and high-risk systems embedded in regulated products such as medical devices and machinery have until 2 August 2028. General purpose AI model rules and Article 50 transparency obligations were not changed and apply from 2 August 2026. Developers building regulated AI systems keep up with shifting compliance deadlines on daily.dev.