Table of Contents
Why Research IT Needs Specialized Storage
Research computing is no longer just about HPC clusters—it’s about data orchestration.
Across genomics, imaging, atmospheric modeling, and social science, research outputs are increasingly data-centric, not just compute-bound.
If you’re a research IT lead, PI, or infrastructure director, you’re likely facing:
- Exploding dataset sizes—from terabytes to petabytes per project
- Cross-institutional collaboration requirements
- Budget pressure from grant-dependent funding
- Toolchain diversity (Nextflow, SLURM, RStudio, Jupyter)
- Retention mandates for reproducibility and compliance
Generic cloud storage often lacks the throughput, file concurrency, and lifecycle control that scientific workflows demand.
And if you’re managing shared infrastructure, your choices impact dozens—if not hundreds—of research teams.
What Research Teams Really Need from Storage in 2025
| Requirement |
Why It Matters |
| Multi-petabyte scalability |
Instrument-generated data sets (e.g., sequencers, telescopes) can’t hit capacity walls |
| POSIX + object flexibility |
Need file-based access for legacy tools and object for cloud-native ML |
| Concurrent access performance |
Simultaneous reads/writes from SLURM jobs, notebooks, or pipelines |
| Cold storage integration |
Archive datasets from past grants without incurring full-cost tiers |
| Policy-based data governance |
Set quotas, share across departments, enforce retention timelines |
| Open science (FAIR) compatibility |
FAIR principles, DOI assignment, versioning for published data |
IBM Spectrum Scale (GPFS)
Best for: Universities and national labs with HPC-driven workflows and on-prem infrastructure.
Used across thousands of research institutions, IBM Spectrum Scale
(formerly GPFS) supports concurrent I/O at massive scale.
It allows you to share files across compute clusters, retain archival datasets on tape, and enforce quotas per lab or PI.
Key Capabilities:
- High-performance parallel file system
- Tiering across flash, disk, and tape
- POSIX-compliant with policy-based quotas
- Used in genomics, particle physics, chemistry
VAST Data
Best for: Research centers combining hot + cold datasets, AI/ML workloads, and GPU clusters.
VAST Data
is increasingly deployed in research facilities where file size, performance, and endurance vary drastically.
It’s used for microscopy, cryo-EM, LLM training, and large-scale imaging workloads.
Key Capabilities:
- Exabyte-scale flash-based file + object system
- No need for tiering: all data lives in a high-performance namespace
- Works natively with Kubernetes and Apache Spark clusters
- Accelerates access for TensorFlow and PyTorch pipelines
AWS S3 + Open Data Program
Best for: Hosting, sharing, or collaborating on globally accessible datasets.
If your research involves open collaboration, reproducibility, or AI-ready data pipelines,
Amazon S3 is a de facto standard.
The AWS Open Data Program
also hosts curated research datasets—from Landsat to the 1000 Genomes Project.
Key Capabilities:
- Object storage with fine-grained access control
- Lifecycle rules for long-term archival (S3 Glacier, Deep Archive)
- Data versioning for publication-grade datasets
- Globally distributed for multi-university collaboration
DDN EXAScaler
Best for: Physics, climate, and chemistry simulations on Lustre-based HPC.
DDN EXAScaler
powers some of the world’s most advanced simulations—like quantum dynamics and weather modeling—via a high-performance file system built for I/O-intensive compute jobs.
Key Capabilities:
- Lustre-based parallel file system
- RDMA support for massive throughput
- Supports burst buffer architectures
- Often deployed in DOE, NASA, and national research clusters
Google Cloud Storage + Public Datasets
Best for: AI/ML-oriented research and big query analysis.
Google Cloud Storage plus
Google Cloud Public Datasets
is ideal for projects that rely on machine learning, rapid querying, or require persistent notebooks.
Key Capabilities:
- Seamless integration with BigQuery, Vertex AI, and Colab
- Bucket lifecycle rules for archival
- Supports genomic analysis via Terra (Broad Institute)
- Common in Earth science, epidemiology, and imaging AI research
Choosing Architecture: Cloud, HPC, or Hybrid?
| Architecture |
Best For |
Watch Outs |
| On-Prem HPC |
SLURM/Lustre clusters with strict latency needs |
High CapEx and IT burden |
| Cloud-Native |
AI/ML + open data workflows |
Hidden costs in egress + long-term retention |
| Hybrid |
Centralize active workloads, offload legacy datasets |
Requires tight orchestration & budget management |
Best Fit by Scientific Domain
Final Take: Your Storage Strategy Is Your Research Strategy
If you’re responsible for enabling research, you’re not just managing petabytes—you’re managing possibilities.
Whether you’re supporting real-time imaging in a lab, distributing genomics data across teams, or preserving decades of environmental records,
the storage choices you make today shape the pace, reproducibility, and openness of your research tomorrow.
The best scientific storage platforms:
- Scale without forcing you into vendor lock-in
- Bridge file-based HPC and cloud-native tools
- Automate cold storage without breaking pipelines
- Support FAIR principles for data sharing and citation
It’s not just about keeping data safe. It’s about accelerating discovery, enabling collaboration, and supporting science that lasts beyond the life of a grant.
Graphic Suggestion:
A “research data hub” diagram with four quadrants:
- Collect (lab instruments, satellites, surveys)
- Compute (HPC clusters, GPUs, notebooks)
- Collaborate (cloud sharing, DOIs, ACLs)
- Preserve (cold storage, compliance, archives)
Each quadrant anchored by one or two representative storage vendors (e.g.,
DDN,
VAST Data,
AWS S3),
with academic-style typography and bold pastel icons.
