Why architecture matters here

Data versioning fails on manual snapshots, unlinked lineage, and cost blowouts. Architecture matters because tool + lineage + registry combine for reproducibility.

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The architecture: every piece explained

The top strip is versioning. Raw data immutable. Versioning tool DVC/Delta/Iceberg. Snapshot ID. Lineage run → data.

The middle row is use. Reproduce checkout old. Compare delta. Metadata + schema. Governance PII + retention.

The lower rows are ops. Registry integration. Metrics. Ops — cost + storage tier + audit.

ML data versioning — DVC + Delta + Iceberg + lineage + reproducibilityknow exactly what data trained your modelRaw dataimmutable landingVersioning toolDVC / Delta / IcebergSnapshot IDcommit / snapshotLineagerun → data versionReproducecheckout old versionComparedelta between versionsMetadata + schemaevolvingGovernancePII + retentionRegistry integrationmodel + data linkedMetricscoverage + freshnessOps — cost + storage tier + auditrevertdiffevolvegovernlinkwatchwatchoperateoperate
ML data versioning + lineage + reproducibility.
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End-to-end flow

End-to-end: training run pins Iceberg snapshot ID. Model registry stores link. Six months later, reproduce: checkout snapshot; retrain; verify same metrics. Compare snapshots shows what data changed. Governance verifies PII rules.