Data Pipeline QA Factory
Data pipeline validation with schema checks, quality testing, and anomaly detection
About
A seven-stage quality assurance pipeline for data pipelines. Schema validation and sample profiling feed into parallel anomaly detection and business rule checks. Findings are consolidated into a QA report, reviewed by a reflection gate, and finally submitted for human approval before pipeline promotion.
Input / Output
Input
Data pipeline configuration or dataset to validate
data_pipelineOutput
QA report with validation results, anomaly findings, and recommendations
min quality: 0.85Pipeline Stages
schema validation
ExecuteValidate data schemas, types, and structural integrity
sample profiling
ExecuteProfile data samples for distributions, nulls, and cardinality
anomaly detection
ExecuteDetect statistical anomalies, outliers, and unexpected patterns
quality rules
ExecuteApply business-specific quality rules and constraints
qa report
ExecuteGenerate comprehensive QA report with findings and severity ratings
quality gate
ReflectReview QA report completeness and accuracy
approval
ApprovalHuman approval before pipeline promotion