MetaKarta computes end-to-end, column-level lineage by parsing your actual code artifacts. SQL scripts, ETL mappings, stored procedures, BI models. It doesn't wait for jobs to run. It doesn't rely on logs. It reads what's there. Your lineage is always complete.
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Log-based lineage only shows you what ran. MetaKarta shows you what's designed to run. The parser reads SQL scripts, Python, Informatica mappings, SSIS packages, stored procedures, and COBOL copybooks directly. It stitches the full chain together: source through ETL, through the warehouse, into BI models, out to reports, dashboards, AI models, and embedded applications.
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The lineage engine reads your code artifacts directly. No instrumentation, no waiting for jobs to run.
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Metadata is automatically stitched into an end-to-end lineage graph, column by column, across every system in your estate.
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Run impact analysis on any column before a schema change deploys. See every downstream consumer. Know what breaks.
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Every change is version-controlled, attributed, and timestamped. Answer "what did this look like on March 14th?" in seconds.
Trace every downstream consumer of any column before a schema change deploys. Always complete, never probabilistic.
From Legacy on-prem to modern cloud. Oracle, Snowflake, Databricks, Informatica, SAP BusinessObjects, Tableau, and more.
End-to-end lineage from source through ETL/ELT, through the warehouse, into BI models, and out to every consumer.
Every change logged, attributed, timestamped, and reversible. Reconstruct your estate's state at any point in history.
Data Warehouse, Data Lake, Data Vault, on-premises, cloud, and hybrid. Covers the estate you have, not an idealized one.
Trace every AI model output back through governed definitions to the source data that produced it.
Only shows you what ran
Log-based systems reconstruct lineage from runtime telemetry: job execution logs, query histories, runtime events. If a job didn't run, it's invisible. If a system doesn't emit logs, it's a gap.
Drawbacks
Dormant jobs, unscheduled pipelines, and non-logging systems disappear from your lineage map
It covers what ran recently, not what exists
Impact analysis starts after a schema change touches production, not before
False positives from log artifacts create noise that requires manual verification
Reads what's designed to run
MetaKarta's parser reads source code artifacts directly. SQL scripts, ETL configs, stored procedures, BI semantic models. It doesn't depend on execution history. The estate is fully mapped whether a job ran last night or hasn't run in six months.
Advantages
Complete lineage, with no blind spots from dormant or non-logging systems
Impact analysis shows what breaks before any change deploys in production
Version control lets you answer "what did it look like on this date"
No integration needed between lineage, catalog, governance, and semantic definitions
Cloud migration impact analysis
Identify every downstream consumer of every column before a schema change deploys. No surprises after go-live.
BI platform migration
Trace the full lineage of every report and dashboard before you decommission a source system, so nothing breaks in the move.
Legacy system decommissioning
Map every dependency on aging on-prem infrastructure before you pull the plug.
Regulatory traceability (GDPR, SOX, BCBS 239)
Trace any reported number back to its source with a complete version-controlled audit trail.
AI audit & provenance
Prove AI outputs from the source data, transformations, and governed metadata that produced it. Audit-ready on demand.
Root cause analysis
When a dashboard number is wrong, find the broken transformation in minutes, not days of manual reconstruction across disconnected tools.
Schema change governance
Run automated impact analysis before any schema change deploys. See every affected downstream consumer.
Estate documentation for architecture teams
Maintain a version-controlled picture of your architecture, without manual documentation every time something changes.
Semantic grounding for AI
Feed lineage-traced data context to LLMs so every AI response is grounded with verifiable provenance from source to output.
MetaKarta establishes the shared metadata foundation organizations have been trying to patch together for years: trusted BI, reliable AI, and defensible governance, compiled into every architecture, every cloud, and every tool. No runtime dependency. No vendor lock-in. No greenfield start required.
Log-based tools infer lineage from runtime telemetry, so coverage depends on what happened to run while they watched. MetaKarta parses the source code itself: SQL, ETL configurations, stored procedures, BI models. The result is complete by construction, including dormant jobs and systems that emit no logs.
SQL scripts, Python, Informatica mappings, SSIS packages, stored procedures, COBOL copybooks, and BI semantic models, across 400+ native connectors spanning legacy on-prem and modern cloud.
Yes. Pick any column and see every downstream consumer, across systems, at column level, before the change ships. Computing lineage from the code itself is what makes the pre-deploy answer complete.
Yes. Every change to the metadata estate is versioned, attributed, and timestamped. Reconstructing the state of any pipeline, column, or definition is a query, and it takes seconds.
Yes. AI provenance traces a model's output back through the governed definitions it consumed to the source data underneath, which is what an audit committee asks for when it asks whether an AI answer can be trusted.