RAWWorkflows

RAWWorkflows is a term used to describe a class of frameworks and practices for designing and executing automated workflows on raw data and raw media. It encompasses methods and tools that manage the end-to-end processing of unprocessed inputs, with an emphasis on reproducibility, provenance, and efficiency. The term is not tied to a single product, but rather to a family of approaches adopted across different domains.

Core concepts include modular pipeline design, configuration-driven execution, and plug-in architectures. Pipelines are defined by declarative configurations (often JSON or YAML) that specify processing steps, parameters, and dependencies. A key objective is to enable traceability of results through comprehensive metadata, versioning of pipelines and data, and auditable execution histories.

Architectures typically feature an orchestration layer that schedules tasks, an execution engine that runs processing steps, and a plugin system that provides domain-specific processors. Support for containerization, scalable storage, and compute backends (local, on-premises, or cloud) is common, as is data provenance, access control, and error handling to maintain reproducibility across environments.

Applications span several domains. In photography and media, RAWWorkflows describe automated raw image or video processing pipelines. In scientific imaging and industrial sensing, they refer to standardized handling of raw detector data, including calibration, quality control, and metadata capture. Geospatial imagery and other data-intensive fields also adopt similar concepts to streamline raw-data processing.

Adoption is diverse and pragmatic; there is no universal standard labeled RAWWorkflows. Instead, organizations tailor the concept to their domain, balancing openness, interoperability, and governance. Practitioners often document configurations, maintain versioned pipelines, and integrate with existing data-management and quality-assurance practices.