A schema change is sitting in a pull request. Someone has to say which dashboards, pipelines, and models it breaks, and your lineage tool only knows what ran during its last observation window.

MetaKarta's Data Lineage reads the code that moves your data and computes the trace from it, column by column, before a query runs. Lineage by design, not by observation.

What the trace usually looks like

Impact analysis means hunting through pipelines and hoping the list is complete. When a board number moves overnight, the investigation starts at the report and works backward through systems that each hold a partial view.

Log-based lineage records what executed. A quarterly job, a rarely used branch, or a filter that governs data without moving it stays invisible until it causes an incident.

That gap comes from how the trace is collected, so tuning the tool won't close it.

How MetaKarta computes lineage

MetaKarta reverse-engineers the estate from the artifacts that define it: SQL scripts, stored procedures, ETL mappings, BI semantic models, and notebooks. Transformation parsers cover SQL across on-premises and cloud databases (Microsoft SQL Server Transact-SQL, Oracle PL/SQL, Teradata BTEQ, Snowflake SQL) plus Python and Scala in Databricks. Data flow emulators cover the transformations inside Informatica PowerCenter, IBM DataStage, Qlik Talend, dbt, and Matillion.

Nothing has to run first.

Column level first. Every column carries its own trace, down to the join, filter, or expression at each step. Table and schema views are built from those column traces, so a table-level question still gets a column-level answer.

Control flow included. A column can decide what moves without moving itself, through a WHERE clause, a filter, or a lookup. MetaKarta surfaces those dependencies in impact analysis, which is where most consumer lists come up short.

Data flow and semantic flow together. One diagram shows how data moves physically and how a business term maps down to the columns that implement it. The semantic view lets lineage answer "which definition is this report using?"

Versioned history. MetaKarta versions every lineage model under configuration management. Compare today's estate with its state six months ago and see what was added, changed, or removed.

A definition you can't trace is a definition you can't trust.

What it delivers

Trusted BI. Reliable AI. Defensible governance.

Trusted BI. Any number traces to its source in seconds, at column level, with the code that produced it on screen.

Reliable AI. Lineage runs from source through transformation to the governed definition an agent consumed, so an answer traces back to the columns behind it.

Defensible governance. The full flow history sits behind every audit answer, versioned and attributed.

One repository under every capability

Data Lineage reads from and writes to the same shared metadata repository as Data Catalog, Data Governance, and Semantic Hub. The catalog entry, the owner, the glossary term, and the sensitivity label hang off the same object you're tracing.

Impact analysis returns the consumers along with the people accountable for each one. When governance labels a column as PII, the label travels the trace and shows every place that data lands downstream.

When a governed definition changes, lineage shows every report and model that depends on it. One metamodel keeps those views aligned, with no sync job between them.

Coverage across the estate

400+ native connectors span legacy on-premises systems through modern cloud platforms and BI tools, including Oracle, Microsoft SQL Server, Teradata, SAP HANA, Snowflake, Databricks, Informatica PowerCenter, IBM DataStage, Qlik Talend, dbt, Power BI, Tableau, MicroStrategy, SAP BusinessObjects, and IBM Cognos.

That library comes from nearly 30 years as the OEM metadata engine embedded in Microsoft Purview, Informatica from Salesforce, IBM, Oracle, and Qlik Talend. It was built to production standards inside those products before MetaKarta shipped as a standalone platform.

Where teams put it to work

Impact & Root Cause Analysis. Run impact on a column before a change deploys and export the consumer list into the change ticket. When a dashboard number moves, trace it back to the expression that changed. Know the blast radius before the first ticket lands.

Audit & Compliance. Trace a reported figure for SOX, GDPR, or BCBS 239 back through the parsed flow. Version comparison answers "what changed," and the dependency data answers "what did it affect."

Data & BI Platform Migration. Trace from source tables to the report fields that consume them, and the terminal nodes become the real dependency list for the move.

BI Metric Consistency. Trace two conflicting versions of one metric back to source, and the filter or join that separates them shows up on screen.

AI Context & Governance. Trace an agent's answer back through the governed definitions it used to the columns and code underneath.

Proof points

• Parser-based lineage computed from the code, column level and cross-system
• Control-flow dependencies (filters, WHERE clauses, lookups) included in impact analysis
• Data flow and semantic flow on one diagram
• Version and configuration management with side-by-side model comparison
• 400+ native connectors, legacy on-premises through modern cloud
• Nearly 30 years as the OEM engine in Microsoft Purview, Informatica from Salesforce, IBM, Oracle, and Qlik Talend

What a complete trace changes

Lineage computed from the code gives every team the same answer to "where did this come from," whether the question arrives in a change ticket, from an auditor, or in an AI model review. One trace serves all three.

MetaKarta's Data Lineage computes that trace across 400+ connectors, on the same shared metadata repository as Data Catalog, Data Governance, and Semantic Hub.

Get in touch to learn more about tracing a number in your own estate back to the line of code that produced it.