data-quality-evaluator

What problem does it solve?

Evaluation framework for streaming data quality assessment and benchmarking. Use when evaluating data quality metrics, detecting anomalies in streaming pipelines, measuring data drift, assessing completeness/accuracy/timeliness, or comparing data quality across different pipelines or configurations. Applies to both Context-Aware and Streaming Data Quality research domains.

Core Features & Use Cases

• Core metrics framework covering Completeness, Accuracy, Timeliness, and Consistency.
• Context-aware metrics and drift/anomaly detection support for streaming data.
• Use Cases: benchmarking data quality across pipelines, validating data pipelines, and QA for streaming workloads.

Quick Start

Run the evaluation framework against your streaming pipeline to generate a comprehensive data quality report.