Principle:Ucbepic Docetl Pipeline Configuration
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Configuration |
| Last Updated | 2026-02-08 01:40 GMT |
Overview
A declarative specification principle that defines the structure, operations, and data flow of an ETL pipeline through configuration schemas.
Description
Pipeline Configuration is the practice of describing an entire data processing pipeline declaratively—specifying datasets, operations, execution steps, and output targets without writing procedural code. In DocETL, this is achieved through YAML configuration files validated against Pydantic schemas. The declarative approach enables:
- Separation of pipeline logic from execution infrastructure
- Validation of pipeline structure before execution
- Optimization and rewriting of pipeline configurations by automated tools
- Portability between CLI, Python API, and playground environments
The key schemas are PipelineStep (which sequences operations), PipelineOutput (which configures where results are written), and PipelineSpec (which combines steps and output into a complete specification).
Usage
Apply this principle when defining a new DocETL pipeline. Every pipeline requires at minimum: one dataset, one or more operations, pipeline steps that reference those operations, and an output configuration.
Theoretical Basis
Declarative pipeline configuration follows a schema-validated approach:
- Schema Definition: Define typed schemas for all configuration components
- Composition: Combine schemas into a complete pipeline specification
- Validation: Use schema validators to catch errors before execution
- Interpretation: Pipeline runner interprets the validated configuration
# Pseudo-code for declarative pipeline configuration
schema = validate(yaml_config)
pipeline = PipelineSpec(
steps=[PipelineStep(name, operations, input) for step in schema.steps],
output=PipelineOutput(type, path)
)