Data lineage and schema inference for enterprise data teams
Automated extraction and visualization of data architecture (schemas, lineage, flow diagrams) from live databases and data warehouses, eliminating manual modeling for data governance and compliance workflows.
The problem
Data architects and governance teams manually document data lineage, schemas, and flows via spreadsheets, Visio diagrams, and reverse-engineering sessions, consuming weeks per initiative and falling out of sync with actual infrastructure. DAMA-certified professionals spend a substantial portion of project time on discovery and documentation rather than design. Most organizations have no single source of truth for data relationships, ownership, or compliance metadata.
Who has it: Mid-market and enterprise data governance teams (50–500 data engineers and analysts) at financial services, healthcare, and manufacturing firms required to maintain DAMA-compliant metadata and pass regulatory audits.
Why now: Explosion of multi-cloud and hybrid data stacks (Snowflake, BigQuery, Redshift, Databricks) with no unified metadata layer; regulatory tightening (GDPR, HIPAA, SOX) requiring auditable lineage; and rise of data mesh architectures forcing teams to map dependencies across hundreds of tables and pipelines.
Where this came from
2 public sources behind this idea.
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