Column-level lineage, cross-system, computed from source code
Covers systems whose jobs haven't run recently
400+ connectors across legacy, cloud, and hybrid sources
Root cause traced to the specific transformation
A column gets renamed, a table gets restructured, and the downstream damage surfaces in production when a dashboard breaks or a report goes out wrong.
After the incident, root cause means tracing by hand through disconnected systems: job logs, tribal knowledge, whoever happens to be on call that week.
The analysis that should have run before the change ends up running after something already broke.
MetaKarta parses the actual code: SQL, ETL logic, stored procedures, BI definitions. The dependency graph reflects what was designed. Log inference reports what happened to execute inside an observation window.
That makes impact analysis complete by construction. Every downstream consumer of a column appears, including the ones sitting in systems whose jobs haven't run in months.
When something breaks anyway, the same engine runs in reverse: from the wrong number on the report back to the transformation that produced it.
Know the blast radius before the first ticket lands.
The trace from a broken number to its transformation is a query against the repository.
A definition you can't trace is a definition you can't trust.
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 queries that execute during an observation window, so anything that didn't run in that window is missing. MetaKarta parses the code itself (SQL, ETL logic, stored procedures, BI definitions), so the dependency graph includes everything that was built, including the job that runs once a quarter.
Column level, across systems, traced to the line of code that defines each transformation. One column in a Snowflake table can be followed upstream to its sources and downstream to every view, ETL job, semantic model, and report that reads it.
At design time, before the change deploys. Because the graph is computed from code, a proposed rename or restructure can be checked against every downstream consumer while it's still a proposal.
Yes. BI definitions are parsed along with the warehouse and ETL code, and governed metrics sit in the same shared metadata repository, so a column change shows which reports, which metrics, and which compiled targets it affects.
The same lineage runs in reverse. Start from the wrong value on the report, follow it back through each transformation to the one that changed, then check version history for who changed it and when.
400+ connectors across legacy, cloud, and hybrid sources, from mainframe-era databases and ETL tools through Snowflake, Databricks, Power BI, and Tableau. The Connector Explorer lists every supported system.
Ask MetaKarta lets business and technical users ask about lineage in plain language. MetaKarta MCP's Metadata Management Tools expose the same lineage to external AI agents, with every response permissioned to the person behind the request.
