Stream DaQ: Stream-First Data Quality Monitoring

TL;DR

Stream DaQ introduces a novel, real-time data quality monitoring framework tailored for unbounded data streams, enabling dynamic, context-aware assessments that outperform existing static and batch-oriented approaches.

Contribution

The paper presents a new streaming data quality model with configurable windowing and adaptive constraints, implemented in an open-source Python framework that unifies fragmented quality checks.

Findings

Significantly faster execution time and higher throughput than existing solutions.

Provides richer, context-aware quality assessments through native streaming capabilities.

Seamlessly integrates with modern data science workflows.

Abstract

Data quality is fundamental to modern data science workflows, where data continuously flows as unbounded streams feeding critical downstream tasks, from elementary analytics to advanced artificial intelligence models. Existing data quality approaches either focus exclusively on static data or treat streaming as an extension of batch processing, lacking the temporal granularity and contextual awareness required for true streaming applications. In this paper, we present a novel data quality monitoring model specifically designed for unbounded data streams. Our model introduces stream-first concepts, such as configurable windowing mechanisms, dynamic constraint adaptation, and continuous assessment that produces quality meta-streams for real-time pipeline awareness. To demonstrate practical applicability, we developed Stream DaQ, an open-source Python framework that implements our theoretical model. Stream DaQ unifies and adapts over 30 quality checks fragmented across existing static tools into a comprehensive streaming suite, enabling practitioners to define sophisticated, context-aware quality constraints through compositional expressiveness. Our evaluation demonstrates that the model's implementation significantly outperforms a production-grade alternative in both execution time and throughput while offering richer functionality via native streaming capabilities compared to other choices. Through its Python-native design, Stream DaQ seamlessly integrates with modern data science workflows, making continuous quality monitoring accessible to the broader data science community.