Datadepend

Datadepend is a term used to describe a framework and set of practices for managing dependencies between data artifacts and the computational tasks that consume or produce them within data-driven workflows. It encompasses the specification of inputs, outputs, versions, and provenance, enabling reproducibility and traceability across pipelines.

Key concepts include dependency graphs that encode how datasets, models, and results relate, a metadata store for lineage and versioning, and rules that determine when downstream tasks should rerun. Datadepend frameworks typically support incremental processing, caching, and conflict resolution when datasets are updated.

Typical architectures consist of a data source layer, a transformation layer with directed acyclic graphs, a scheduling or orchestration layer, and a metadata and policy layer. Integrations with data catalogs, artifact stores, and instrumented logging are common. Principles of data quality, access control, and governance are often incorporated to ensure secure and compliant data handling.

Use cases span data pipelines, machine learning experiment tracking, feature stores, and analytics dashboards that rely on consistent data snapshots. Benefits include improved reproducibility, easier impact analysis, and stronger governance; drawbacks include added system complexity and the need for discipline in naming and versioning conventions.

History and status: The term has appeared in scholarly and practitioner discussions and in some open-source tools that aim to manage data dependencies. It is not yet standardized, and various projects implement its concepts with different data models.

See also: data provenance, data lineage, data governance, workflow management, feature store.