Question 1

Which is the best data platform for clinical genomics in 2026: Snowflake, Databricks, or Microsoft Fabric?

Accepted Answer

The best platform depends on the dominant workload. Databricks is a strong fit for population genomics, VCF processing at scale, and machine learning on unstructured omic data. Snowflake is well suited to clinical analytics, real-world evidence, governed data sharing, and structured multi-omic workloads. Microsoft Fabric is a strong option for Microsoft-native health systems where FHIR-first analytics, Azure, and Power BI are already central. Hybrid architectures are also increasingly viable.

Question 2

Why is Databricks a strong fit for genomics-heavy workloads?

Accepted Answer

Databricks supports Spark-based genomic processing through tools and patterns such as Project Glow, Hail, Delta Lake, and custom VCF pipelines. Its ML capabilities, including Mosaic AI and MLflow integration, make it suitable for teams training or deploying models on internal genomic and clinical data. It is commonly used for computationally intensive workloads involving VCF, BAM, FASTQ, single-cell, and imaging data.

Question 3

When is Snowflake the better choice for clinical genomics?

Accepted Answer

Snowflake can be a strong choice when structured clinical analytics, real-world evidence, governed collaboration, and secure external data sharing are the primary needs. Its secure data sharing model can support collaboration without copying data, while Cortex AI supports AI-assisted analytics on governed data. Snowflake may require additional tools or custom engineering for raw genomic file processing and NGS pipeline orchestration.

Question 4

When does Microsoft Fabric win over Databricks or Snowflake?

Accepted Answer

Microsoft Fabric is often a strong fit for organizations already standardized on Microsoft 365, Azure, Power BI, and Microsoft governance tooling. Its Healthcare Accelerator supports FHIR, DICOM, and OMOP-oriented clinical analytics patterns, while OneLake and Fabric capacity pricing can simplify operations. It is generally less specialized for raw VCF processing and highly computational genomic workflows.

Question 5

Is a hybrid Snowflake, Databricks, and Microsoft Fabric architecture viable for clinical genomics?

Accepted Answer

Yes. Hybrid architectures are increasingly practical when an organization has distinct genomic processing, clinical analytics, AI, and research-sharing requirements. Common patterns include Databricks for genomic processing with Snowflake for clinical analytics and governed sharing, or Microsoft Fabric for FHIR ingestion with Databricks for downstream machine learning. Open table formats and interoperability patterns can reduce unnecessary data copying, but the architecture still needs clear governance, lineage, security, and cost controls.

Question 6

What should a clinical genomics team evaluate before choosing a data platform?

Accepted Answer

Teams should evaluate their dominant data types, including VCF, BAM, FASTQ, FHIR, HL7, DICOM, and structured clinical records; their AI and ML roadmap; integration needs with LIMS and EHR systems; compliance and audit requirements; cross-organization data-sharing needs; cloud and Microsoft ecosystem investments; migration requirements from on-premises HPC; and expected storage and compute patterns at scale.

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