SDhave

SDhave is a term that appears in some technical discussions to describe a framework or concept for secure, structured handling and evaluation of data within software development and data science workflows. It is not tied to a single officially recognized standard or project, and its precise meaning can vary by context.

In general, discussions of SDhave emphasize data provenance, access control, auditable processing, and reproducibility. Proponents describe a set of guiding principles or component layers that aim to ensure that data products can be traced from source to result and that analyses can be independently validated.

Possible components of an SDhave-oriented approach include a defined data model or schema, interface specifications for data input and output, governance policies for access and retention, and tooling for validation, logging, and auditing. Some descriptions also mention template workflows for data ingestion, transformation, and analysis to promote consistency across teams.

Origins of the term are informal; it has been used in forums, speculative proposals, and non-normative documentation rather than in formal standards bodies. As such, there is no universally adopted specification or implementation associated with SDhave.

See also: data provenance, reproducible research, data governance, audit trails.