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Scaling biomedical research on AWS: A cloud-native approach to scientific data management

Scaling biomedical research on AWS: A cloud-native approach to scientific data management

Modern biomedical research generates terabytes of multimodal data—from brain imaging and electrophysiology to genomics and clinical phenotypes. The infrastructure supporting this work must keep pace with the scale, complexity, and governance requirements of contemporary science.

This post explores how cloud-native architectures built using Amazon Web Services (AWS) help research institutions manage, curate, publish, and analyze complex scientific datasets at scale.

The challenge: Academic research needs production-grade infrastructure

Academic environments operate under strict funding constraints and governance requirements. Investigators must justify every dollar of taxpayer funding, and infrastructure decisions must balance innovation with accountability. As a result, many research groups default to local compute clusters or institutionally subsidized servers. While initially inexpensive, these approaches limit scalability, reproducibility, and collaboration.

At the same time, expectations have shifted dramatically. Large-scale analytics and artificial intelligence (AI) now require:

  • Elastic compute for large and variable workflows
  • Secure environments for sensitive biomedical data
  • Transparent, per-project cost attribution
  • Reproducible, versioned pipelines
  • Long-term logging and auditability

Unlike industry, where baseline usage is steady and predictable, academic workloads fluctuate significantly. A lab may need to scale up rapidly for a major analysis and then scale down to near zero. Idle cost must remain minimal, yet the system must be able to expand instantly when needed.

Compounding this challenge, scientists and clinicians are not software developers or DevOps engineers. They primarily work in Python and R, starting with local scripts for exploratory analysis. Transitioning from those scripts to scalable, secure infrastructure should not require major code rewrites or deep knowledge of cloud architecture.

The solution: Cloud-native analytics on AWS

A cloud-native scientific data management platform on AWS is designed to help researchers integrate, organize, curate, analyze, share, and publish complex scientific datasets in a secure and collaborative environment. Such platforms support multimodal data and flexible metadata schemas, so researchers can capture rich contextual information linking files to structured annotations and experimental descriptions.

Central to effective research data platforms is a rich, flexible metadata framework built around JSON Schema–based models that allow researchers to formally define and validate the structure of their data. Each metadata record is stored as a structured JSON document and supports versioning, so that changes over time are tracked and auditable.

AWS architecture: Secure, elastic, and cost-efficient by design

Research analytics infrastructure on AWS can allow users to deploy compute resources directly into their own AWS accounts through a “bring your own compute” (BYOC) model. Institutions and research groups connect their AWS accounts to the platform and provision dedicated compute nodes within those accounts. This approach allows researchers to retain full ownership of their infrastructure, data locality, security policies, and cost controls, while the platform manages orchestration and workflow logic.

Each compute node provisions a self-contained AWS environment using Terraform and infrastructure-as-code (IaC). Workflows run on a combination of:

When a workflow is triggered, Step Functions dynamically constructs a state machine representing a directed acyclic graph (DAG) of processors. Each processor runs in an isolated container or Lambda function, reading from and writing to shared EFS storage. The platform automatically handles dependency resolution, data transfer, credential injection, logging, cleanup, and cost estimation.

Researchers register containerized workflows—derived from existing Python or R scripts—with minimal modification. The infrastructure scales automatically based on workload demand and terminates when processing completes.

Deployment modes for research and compliance

To support diverse institutional requirements, teams can deploy compute nodes in multiple configurations:

  • Basic Mode – Low-cost development environments using default VPC configurations
  • Secure Mode – Dedicated VPC with private subnets, NAT Gateway, and VPC Flow Logs
  • Compliant Mode – No internet access, VPC endpoints only, full audit logging

This flexibility allows the same analytics solution to support exploratory research, production clinical pipelines, and regulated environments with strict network isolation requirements.

Zero idle compute cost and cost transparency

A critical design goal for academic analytics infrastructure is avoiding the risk of paying for unused compute. Because compute nodes rely on serverless orchestration and task-based execution, ECS Fargate and Lambda incur no cost when idle. There are no always-on servers consuming budget. Infrastructure scales up rapidly when workflows run and scales down immediately upon completion.

Each workflow execution generates a detailed cost estimate, which the system logs automatically. Researchers can transparently allocate expenses to specific grants, projects, or research groups, with cost visibility embedded directly into the workflow lifecycle.

Impact: Transforming immune health profiling

This architecture is already at work in immune health profiling research, where investigators use high-dimensional immunophenotyping and advanced modeling to extract meaningful biological signals from complex immune datasets.

Prior to adopting cloud-native workflows, generating a comprehensive immune profile report required approximately three days of manual effort per patient sample. This process combined data preparation, analytic script execution, result aggregation, and expert review, and was time-intensive and difficult to standardize and scale.

Today, raw data is automatically uploaded to the platform, and the workflow engine triggers secure, containerized analytic pipelines. Preprocessing, normalization, modeling, and report generation occur in a reproducible, scalable cloud environment. The system automatically generates a draft immune profile report and routes it to domain experts for final review and sign-off.

The results include:

  • Improved efficiency: What once took three days per sample now completes in minutes
  • Stronger standardization: Automated pipelines deliver consistent quality control
  • Reduced turnaround time: Researchers receive results faster, accelerating discovery
  • Foundation for scale: What was feasible for tens of datasets now scales to hundreds or thousands

Pennsieve, a scientific data management platform at University of Pennsylvania, implements this architecture. See the full write-up for additional details of the platform and its implementation.

Working with AWS: Accelerating scientific infrastructure

AWS has helped research institutions evolve into production-grade scientific operations capable of supporting large-scale biomedical research. This collaboration extends beyond technical guidance into long-term architectural strategy, particularly as institutions expand their footprints within NIH-funded initiatives.

AWS works closely with research institutions to design and deploy Secure Research Environments that support HIPAA-compliant operations. AWS provides architectural guidance for implementing secure multi-account strategies, identity and access controls, and compliance-aligned deployment models that integrate with institutional governance frameworks.

Making scalable compute approachable

Cloud infrastructure has reshaped industry, but adoption in academia has been slower due to concerns about cost, complexity, and operational overhead. This approach to research data management on AWS demonstrates that when cloud best practices are embedded directly into a scientific data management solution, researchers gain:

  • Elastic compute without infrastructure management
  • Transparent cost attribution
  • Reproducible containerized pipelines
  • Secure and compliant environments
  • Integrated logging and auditability
  • Reduced cost and increased efficiency

As biomedical research continues to scale in complexity and data volume, AWS accelerates science by making secure, scalable compute accessible to the research community, without requiring researchers to become DevOps engineers. To learn more about how AWS supports researchers with cost-effective, scalable, and secure compute, storage, and database capabilities, visit Research and Technical Computing on AWS.

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Joost Wagenaar

Joost Wagenaar

Joost Wagenaar, PhD, is an Assistant Professor of Informatics at the University of Pennsylvania who leads development of the Pennsieve platform — a scalable, secure, and collaborative system for managing and analyzing complex biomedical datasets across academia, clinical environments, and global research consortia. His work bridges the gap between proof-of-concept analytics and production-grade research infrastructure by abstracting cloud computing, distributed storage, and data integration complexities, freeing clinicians and scientists to focus on discovery rather than engineering. He has contributed to large-scale NIH-funded initiatives in epilepsy, neuroscience, and multimodal data integration, while advancing Common Data Elements and interoperable data frameworks.

Stephen Aux

Stephen Aux

Stephen Aux is a Principal Account Manager at Amazon Web Services. In his role, he partners with higher education institutions and public sector organizations to accelerate digital transformation and unlock the potential of emerging technologies including Generative AI to drive groundbreaking research, reimagine the learning experience, and expand access to education.

Vinod Kisanagaram

Vinod Kisanagaram

Vinod Kisanagaram is a Senior Solutions Architect at AWS. He currently works with Worldwide Public Sector Enterprise customers to craft highly scalable and resilient cloud architectures. He is passionate about Cloud Operations, AI/ML, and Serverless technologies.