What data infrastructure do biopharma companies need for omics and genomics data at scale?

Summary

• Biopharma omics and genomics workloads need a unified lakehouse foundation that brings genomic sequences, clinical records, imaging, lab systems (LIMS/ELN), and real-world evidence into one open, governed platform.
• Delta Lake provides open, reliable, high-performance storage for both batch and streaming genomics data — from raw processing through large-scale statistical analysis and machine learning.
• Unity Catalog governs all data types, ML models, and AI agents with fine-grained access controls, automatic lineage, and secure sharing — supporting FAIR data principles and reproducibility for regulated research.
• Scalable cloud compute with the Photon engine accelerates Spark-based genomic pipelines and open bioinformatics toolkits such as Project Glow (for example, genome-wide association studies), while automated cluster management reduces infrastructure overhead.
• Delta Sharing and Clean Rooms enable secure, privacy-preserving collaboration across research partners, and Mosaic AI supports multi-omics foundation models and research agents on the same governed data.

What data infrastructure do biopharma companies need for omics and genomics data at scale?

Omics and genomics research generates large, multi-modal datasets, and the journey from molecule to market crosses many data modalities, formats, and regulatory requirements. To manage this at scale, biopharma companies need a unified foundation that can store and process genomic sequences alongside clinical, imaging, laboratory, and real-world evidence data, apply consistent governance for regulated research, and support the statistical analysis, machine learning, and AI that drive discovery. The Databricks Data Intelligence Platform is built on an open lakehouse architecture designed for exactly these needs. See Transforming omics data management with the Databricks Data Intelligence Platform.

Why Databricks for omics and genomics data at scale

• Unified lakehouse foundation. The lakehouse integrates structured, semi-structured, and unstructured data — genomic sequences, clinical records, medical images, electronic lab notebooks and LIMS, and real-world evidence — into a single platform, combining the flexibility of a data lake with the governance and performance of a data warehouse.
• Open, reliable storage with Delta Lake. Delta Lake is the open storage layer that delivers reliability and performance for streaming and batch workloads, supporting the full spectrum of genomics needs from initial data processing to large-scale statistical analysis and machine learning.
• Scalable compute and bioinformatics. The platform runs Apache Spark with the Photon engine to accelerate distributed genomic pipelines and open bioinformatics toolkits such as Project Glow — for example, running genome-wide association studies (GWAS) for genetic target identification — while automated cluster management lets bioinformaticians and data scientists focus on analysis rather than infrastructure.
• Governance for FAIR, regulated data. Unity Catalog provides unified governance across data, ML models, and AI agents with row- and column-level access controls, built-in search and discovery, and automatic lineage across data and model transformations — supporting the FAIR principles (Findability, Accessibility, Interoperability, Reusability), reproducibility, and regulatory submissions.
• Secure collaboration. Delta Sharing enables open, zero-copy data sharing across research partners without requiring recipients to use the same platform, and Clean Rooms allow privacy-preserving analytics across organizations without exposing raw data.
• AI for multi-omics. Mosaic AI supports pre-training multi-omics foundation models and building agent-based research assistants on the same governed data used for analysis.

Getting started

• Bring omics and associated clinical, imaging, and lab data onto the lakehouse and store it in Delta Lake for open, reliable, high-performance access.
• Govern data, models, and agents with Unity Catalog from the start so research stays findable, access-controlled, lineage-tracked, and reproducible.
• Scale genomic pipelines with Spark and Photon and open bioinformatics toolkits, then extend into machine learning and multi-omics models with Mosaic AI.
• Share and collaborate securely with Delta Sharing and Clean Rooms.

FAQs

What kinds of data can the platform manage for omics research?

The lakehouse supports multi-modal data including genomic sequences, clinical records, medical images, electronic lab notebooks and LIMS data, and real-world evidence, so genomic data can be analyzed together with phenotypic and clinical information.

How does the platform support FAIR data and reproducibility?

Unity Catalog governs data, models, and agents with fine-grained access controls, discovery, and automatic lineage across transformations, which supports the FAIR principles and the reproducibility needed for regulated research and submissions.

How does Databricks scale genomic analysis?

Apache Spark with the Photon engine accelerates distributed genomic pipelines and open bioinformatics toolkits such as Project Glow (for example, GWAS), while automated cluster management removes much of the infrastructure overhead.