[WiP] Data Chapter - HPC

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Pillar & Principles

1. Secure by design: Architected to meet strict regulatory, ITAR, and compliance standards, embedding security and data isolation into every component—from lab to cloud.
2. Globally distributed: Enables distributed collaboration while respecting data sovereignty and regulatory zones, with secure interconnects optimized for compliance and performance.
3. High Interoperability: Designed to connect specialized lab instruments, simulation tools, and enterprise platforms across heterogeneous environments—securely and without friction.
4. Cost Efficient & Scalable: Scales elastically to support burst compute, large simulations, and AI workflows while optimizing cost through dynamic resource allocation and tiered data strategies.
5. Unified UX: Offers a seamless, role-aware user experience—from lab scientists to HPC engineers—ensuring consistent access, visualization, and orchestration across environments.
6. Data Backbone (Data-Centric by Design): Establishes a governed, high-throughput data layer that ensures seamless access and movement of data across the platform — enabling consistent ingestion, transformation, AI-driven learning, synthetic data generation, and simulation workflows.

Data Backbone

1. Main Capabilities
   1. Unified data flow from lab systems, external feeds, and internal platforms.
   2. Interoperable data access across AI engines, ETL/ELT pipelines, and HPC simulations.
   3. Data discoverability and traceability, which is critical for regulated environments.
   4. Performance and low-latency delivery for high-speed compute engines and powerful user workstations.
   5. Security and policy enforcement embedded into the data fabric to meet ITAR and compliance constraints.
2. Architectural approaches

Data Flows

Enablers, Future proof

Gen AI

TBD: collect LLM/GenAI use cases from the Study