Data lineage

Data lineage is Fontana’s record of how every value in a workflow run came to be: which files or connectors supplied it, which transforms and validations touched it, which lookups enriched it, and which nodes on the graph produced the result you see at export time.

Lineage is atomic: attached at row and column level, not only as a workflow summary. That gives auditors and operators a precise answer to how, why, and from where data changed, including when conditional logic sent rows down different processing paths.

End-to-end coverage

Fontana captures lineage across the full data lifecycle:

Stage	What lineage records
Ingress	Files, connector reads, and API inputs (loaded provenance back to the source dataset)
Transform and validate	Operations, lookups, merges, compute steps, schema mapping, and AI-assisted transforms
Conditional processing	Filters, validations, and condition operations that include or exclude rows, and branches of the graph that contributed to each output
Egress	Exported rows remain linked to the same provenance chain through Save File Export and downstream delivery

Every processor step that derives data can emit pointer-based sources. Those sources may themselves have lineage, forming a recursive tree you can walk from an export cell back to original intake.

What is atomic lineage?

Atomic means provenance is stored as pointers to upstream rows and columns, not as copied snapshots of entire datasets. You can trace one cell without duplicating millions of identical records, and each link names the node and, where relevant, the operation that produced the change.

How sources are described

Each lineage link carries a usage type that explains why that source contributed to the result:

Usage	Meaning
loaded	Row or field came from an external source (file upload, connector, API)
transformation	Source value was transformed (for example string, date, or decimal operations)
lookup	Source was matched and enriched from a lookup dataset
merge	Source was combined with other inputs
reference	Source passed through unchanged
computed	Source used in a function or compute step
generated	Source used to generate new values (for example schema mapping or AI output)
filtered	Source passed through filter or validation context (row included or excluded on a branch)

Together, usage types and node references show what data was used to calculate results and which conditional branches were involved when filters, validations, or graph paths split processing.

View lineage in Flow

You do not need to leave the app to inspect provenance:

• Data Lineage panel - select a cell in the data grid; the side panel builds a recursive tree from that cell back through upstream nodes, with usage labels and operation context
• Workflow data view - review port-level datasets per node for the active run
• Audit panel - complementary audit items (validation outcomes, transform events, manual edits) alongside lineage for the same run

Lineage answers provenance; audit items answer events on the row. Both support the Transparent data principle.

Export lineage

Lineage travels with the run. You can export provenance for compliance archives, downstream metadata systems, or independent review:

• OpenLineage - Fontana lineage can be viewed and exported in OpenLineage format, the open standard for data pipeline metadata. Export supports integration with observability, catalog, and governance tools that consume OpenLineage events without custom adapters for Fontana’s internal model.
• Run context - exports are scoped to a workflow run, so evidence matches the same snapshot as your datasets and Save File Export outputs (see Egress)

OpenLineage

OpenLineage is an open framework for capturing run, job, and dataset metadata across data pipelines. Fontana maps atomic workflow lineage into that model so your organisation can consolidate pipeline provenance with other OpenLineage-compatible systems.

Why it matters

• Regulatory and audit review - demonstrate which inputs and transforms produced reported figures
• Incident analysis - isolate when a value changed and which step introduced an error or exception
• Operational trust - operators see the same provenance graph that compliance reviewers export
• Downstream integration - OpenLineage export lets metadata platforms ingest Fontana runs alongside warehouses, orchestrators, and BI tools

Lineage is part of Fontana’s Auditable and Transparent data principles. It complements, but is not the same as, the platform immutable security audit trail (sign-in, admin, cluster events in ImmuDB).

Lineage is not the WORM ledger

Data lineage and port audit items live on the workflow file store (.lineage.json, .audit.json sidecars, canvas validation context). They answer how a value was computed. The WORM ledger in ImmuDB answers who performed privileged platform activity. Do not cite lineage exports as a substitute for ImmuDB audit evidence. See Immutable audit trail and Data retention.

Related documentation

• Storage and processing - Core Fontana data principles
• Ingress - where loaded lineage begins
• Egress - exporting datasets with provenance context
• Compliance evidence - workflow lineage overview
• Security - SOC 2 control summary
• Workflow orchestration - reactive execution and node families