filterstructured

Filterstructured is a term used in data processing to describe a pattern in which structured data is filtered through a sequence of modular components. Each component, or filter, examines the input’s fields and emits a subset or transformed version that still conforms to a defined structure. The approach emphasizes explicit data schemas, composability, and predictable data flow.

Core principles include immutability of data items within the pipeline, stateless filters that produce outputs from inputs, and explicit handling of missing or invalid fields. Filters can perform predicates (selecting items by field conditions), projections (keeping or renaming fields), transformations, and enrichment by adding derived fields. The pipeline orchestrator coordinates data movement and backpressure in streaming contexts.

Typical architectures use a pipeline or graph of filters, with a source providing structured records and a sink collecting results. Filters declare their input and output schemas, enabling early validation and error handling. Some implementations support parallel or asynchronous filtering and have dedicated channels for bad records to keep the main flow clean.

Common use cases include ETL tasks for structured data sources; real-time log processing with per-record filtering; data validation and cleansing; feature extraction for machine learning pipelines; and privacy or compliance filtering that redacts or removes sensitive fields.

Implementation considerations include language and library support for typed data, schema evolution handling, and performance of field-level operations. The approach favors reusable, testable filters and clear data contracts to aid maintenance and extension.

Related concepts include data pipelines, functional programming, and streaming architectures. Although not tied to a single standard, filterstructured appears in frameworks that treat data records as the primary unit of transformation.