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Databricks Glow

Glow is Databricks' open-source distributed genomics toolkit on Spark. It provides Spark-native readers and transformations for common genomics file formats — VCF, BGEN, PLINK — and emits derived outputs as Delta Lake tables that can be joined to clinical / EHR features under one governance surface.

Stub page. Glow is named in the ingested Databricks multimodal post as the genomics modality's ingestion tool inside the governed-Delta- tables-per-modality pattern. For this wiki, the load-bearing content is the architectural role — modality-specific, Spark-distributed, Delta-native — not the Glow API surface.

Role in multimodal lakehouse architecture

"Glow enables distributed genomics processing on Spark over common formats (e.g., VCF/BGEN/PLINK), with derived outputs stored as Delta tables that can be joined to clinical features." (Source: sources/2026-04-22-databricks-multimodal-data-integration-production-architectures-for-healthcare-ai)

Key properties for the pattern to work:

  • Spark-native — scales to genomic dataset sizes without a separate compute tier.
  • Delta-native outputs — no specialty genomics store required; the substrate is the same Delta Lake that holds imaging features, notes entities, and wearables aggregates.
  • Governance-compatible — outputs live under Unity Catalog and inherit its access-control + lineage + audit posture.

Why it matters for sysdesign

Glow is the canonical example of the principle "modality- specific tooling should emit the lakehouse's native table format, not its own storage tier." Any genomics tool that required a separate store would reintroduce the specialty-store-per-modality anti-pattern that patterns/governed-delta-tables-per-modality exists to avoid.

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