Dataparameters

Dataparameters are configurable values that influence how data is retrieved, transformed, or analyzed within a system. They serve as inputs to data processing workflows, enabling customization, reproducibility, and automation. Dataparameters can be supplied by users, derived from context, or defined by automated rules.

They appear in databases, web APIs, data pipelines, and analysis scripts. Dataparameters control filtering, sorting, sampling, time ranges, geographic scope, feature selection, and handling of missing values. They are distinct from metadata, which describes data rather than directing its flow.

Examples include API query parameters like start and end dates, limit, and sort order; SQL clauses such as LIMIT and OFFSET; pipeline configurations for batch size and parallelism; and preprocessing options for normalization, imputation, or outlier treatment.

Good practice emphasizes clear documentation, validation, and sensible defaults. Parameter provenance and versioning support reproducibility, while input validation helps protect against injection and other security risks. Logging and auditing of parameter values aid debugging and governance.

Standards bodies and teams often use parameter schemas or dictionaries to enforce naming, types, and allowed ranges, improving interoperability and maintainability.