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OPEN
Real-Time Streaming Data Applications at Scale – G148

Description

Industry: Defense and Intelligence / Information Technology
Focus Area: Assured Autonomy / Machine Learning
Estimated Budget Range: TBD
Submission Deadline: Wednesday, October 1st, 2025 at 12:00, ET (TBD)
 

SUMMARY

There is a continuous flow of streaming data being generated – from maritime, ground, air, and space sensor assets to remote edge devices. Data-centricity and the insights to be gained from the integration of sensors and systems, requires a robust architecture to create and sustain real-time data streaming applications for increased speed and quality of sense-making and decision-making. A scalable, modular open systems approach has implications for low-latency multi-user defined microservices, multi-domain situational awareness, and multi-sensor command and control. Preferred architectures employ the latest security standards, in-memory computing, and support the processing massive amounts of data, modeling large-scale complex operating domains or networks (i.e., digital twins), and delivering trusted analysis products and outputs.

Patriot Labs is interested in exploring customer and end-user opportunities requiring real-time streaming data applications capable of fusing any available data streams from sensors, systems, and networks. Preferred requirement architectures will employ the latest security standards, in-memory computing, and use case defined data planes for real-time multi-domain, multi-user, multi-sensor applications.

Requirements may build upon data fabric architectures for enterprise and tactical environments. Functionality sought should include the ability to detect and predict convergence effects of multiple data streams. Requirements should include an open-source software architecture with minimal dependencies that can be flexibly deployed cross-enterprise, including cloud, on-prem, edge, and hybrid environments. Desirable attributes may include: (i) data fabric nodes that actively communicate with each other in a cache coherent manner; (ii) continuous real-time compute in response to dynamic events; (iii) the secure preservation of node data and persistent state using built-in transactional memory; and (iv) support a full range of applications via real-time APIs.

Approaches sought could include distributed-mesh networks for data orchestration and intelligent agents for grid sensing. Special consideration given to requirements that include: (i) running locally and entirely in-memory with low latency analytics; (ii) built-in resiliency for operation in denied, disrupted, intermittent, or limited environments; (iii) support for custom analytics that can be tailored to any operating domain, mission, or scenario; (iv) making all data sources accessible to external ML/AI pipelines; (v) utilize network encryption for secure operations; and (vi) MOSA compliance and SWaP advantages.

 

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