datadepends

Datadepends are a formal representation of data dependencies within data processing systems. They encode which data artifacts—datasets, tables, files, or reports—rely on which inputs and transformations. The concept supports provenance, reproducibility, and impact analysis by documenting how outputs are derived.

In practice, datadepends are typically modeled as a directed acyclic graph, where nodes are data artifacts and edges denote a dependency relationship. Edges may carry metadata such as the transformation name, parameters, tool version, run timestamp, and environment. Datadepends can be stored in metadata catalogs, lineage services, or embedded in the data factory’s orchestration layer.

Creation and maintenance are often automated by data pipelines that emit lineage records whenever a run completes or a artifact is stored. They enable use cases such as impact analysis when source data changes, reproducible research, auditing for compliance, and optimization through caching or incremental processing by recognizing unchanged inputs.

Relation to other concepts: datadepends are related to data provenance and data lineage but focus specifically on dependency relationships among data artifacts. They differ from database functional dependencies and are conceptually similar to build graphs used in software tooling. Challenges include scale, non-deterministic transforms, data privacy, and evolving schemas. Example: a dataset C produced by applying transformation T1 to A and T2 to B; the datadepend records A → T1 → C and B → T2 → C with associated metadata.