Genomics Data Storage and Management Market (2026-2036)

The growth of this market is mainly driven by the rapid growth in sequencing output and the resulting volume of raw, aligned, and variant data, the expansion of national genomics programs, biobanks, and newborn and population sequencing that require long-term storage and controlled access, the growth of clinical genomic testing with long data retention requirements, and the shift of genomic data to cloud storage and trusted research environments. In addition, the adoption of long-read, single-cell, spatial, and multi-omic technologies that generate larger and more complex datasets, the use of genomic data for artificial intelligence (AI) model training, the adoption of genomic data compression and lifecycle management to control costs, and data sovereignty and security requirements are further supporting the growth of this market.

The Genomics Data Storage and Management Market, also referred to as the genomic data storage market, sequencing data storage market, or genomic data infrastructure market, comprises the cloud storage services, storage hardware, data management software, and professional and managed services used to store, protect, organize, archive, and provide access to genomic and multi-omic data. The market includes cloud object, file, and archival storage services and genomics-specific cloud storage; on-premise storage systems, including all-flash and hybrid arrays, parallel file systems, scale-out network-attached storage, object storage, and tape libraries; genomic data compression and encoding software; data management, orchestration, tiering, and lifecycle management software; metadata management and data cataloging; backup, archiving, and disaster recovery; and design, migration, and managed storage services. The market covers raw sequencing data, such as base call and FASTQ files, aligned data, such as BAM and CRAM files, variant data, and multi-omic, single-cell, and spatial data. The market ecosystem extends from cloud providers, storage hardware manufacturers, and data management software developers to sequencing centers, genome institutes, national genomics programs and biobanks, hospitals and clinical laboratories, pharmaceutical and biotechnology companies, and academic research institutions.

Genomic data are among the largest and fastest-growing categories of scientific data. A single human whole genome sequenced at standard coverage generates roughly 100 gigabytes or more of raw data, and high-throughput sequencers can generate many terabytes of data per run. Programs sequencing hundreds of thousands of genomes, and research projects using long-read, single-cell, and spatial technologies, generate petabytes of data that must be stored securely, often for decades, and kept accessible for reanalysis. Storage and data management have therefore become a significant part of the cost and complexity of genomics.

National programs and biobanks are major sources of demand. In June 2026, the All of Us Research Program of the U.S. National Institutes of Health (NIH) expanded its dataset to more than 747,000 participants, including 535,000 whole-genome sequences linked to nearly 482,000 electronic health records and more than 1.3 billion genetic variants, all stored and accessed in the cloud through its Researcher Workbench. UK Biobank holds all of its data on its cloud-based Research Analysis Platform (UKB-RAP), and its planned releases for 2027 include long-read whole-genome sequencing with epigenetic data for up to 50,000 participants, proteomic data covering 5,300 proteins for 150,000 participants, and single-cell RNA sequencing data, each adding large new data volumes to be stored and managed. In the U.K., Genomics England is sequencing the genomes of up to 100,000 newborns in its Generation Study and, in 2026, is adding long-read whole-genome sequencing for at least 2,000 samples under its Babies in Focus program, a contract valued at an estimated GBP 4.2 million, creating long-term data storage and retention needs for longitudinal genomic data. These programs require storage that is secure, scalable, and accessible to approved researchers, and they increasingly hold data in the cloud or in trusted research environments rather than distributing copies.

Public data repositories and research funders are also moving to the cloud. The Sequence Read Archive (SRA) of the NIH, the largest public repository of raw sequencing data, has been made fully available in the cloud on Google Cloud and Amazon Web Services (AWS) through the NIH STRIDES Initiative, which provides researchers with access to commercial cloud storage and computing. Cloud storage allows researchers to analyze large datasets where they are stored, avoiding costly and slow data transfer, and cloud providers offer genomics-specific storage and analysis services. At the same time, many sequencing centers, hospitals, and national programs continue to operate high-performance on-premise storage for active analysis, data sovereignty, and cost control, and hybrid architectures are becoming common.

Cost control is a growing priority. Storing large volumes of raw data indefinitely is expensive, and organizations are adopting compression, tiered storage that moves inactive data to lower-cost archival tiers, and policies on which data to retain. In June 2026, Illumina, Inc. reported that DRAGEN ORA lossless compression, available in DRAGEN v4.5, makes sequencing data files up to five times smaller than compressed FASTQ files, saving laboratories approximately USD 900 in storage costs per NovaSeq X 25B flow cell and reducing storage costs by up to 80%. Data management software that tracks data, metadata, and provenance across locations is also becoming essential for reproducibility, compliance, and AI.

The market includes cloud providers such as Amazon Web Services, Google Cloud, and Microsoft Azure; storage hardware and software companies such as Dell Technologies, NetApp, IBM, Hewlett Packard Enterprise, Pure Storage, DataDirect Networks (DDN), and WEKA; and Illumina, which offers genomic data compression and cloud data management. These companies compete on performance for genomic pipelines, scalability, cost per terabyte, data protection and security, compliance with health data regulations, support for hybrid and multi-cloud deployment, and integration with genomic analysis workflows.

At the same time, the market faces challenges, including the high and growing cost of long-term storage and cloud data egress, data privacy and security requirements and differences in data protection laws between countries, data sovereignty requirements that restrict where data can be stored, the complexity of managing data across multiple locations and formats, and uncertainty about which data should be retained over the long term. As a result, companies that combine scalable and cost-efficient storage, compression and lifecycle management, strong security and compliance, hybrid and sovereign deployment options, and integration with genomic workflows are expected to capture the largest share of growth over the forecast period.