DQLabs ensures data quality even before ingestion by detecting issues such as missing values, schema mismatches, and inconsistencies, preventing bad data from entering Azure Data Factory (ADF) pipelines. Once data is in motion, DQLabs continuously monitors its health, accuracy, and consistency, identifying anomalies in real-time to ensure that data remains reliable throughout the pipeline. By applying predefined business rules and AI-driven quality checks at multiple pipeline stages, DQLabs automatically detects and flags data quality issues before they impact downstream processes.

With full visibility into data transformations and movement across ADF pipelines, DQLabs enables organizations to track data lineage, enforce governance policies, and maintain compliance. The lineage plays a crucial role in issue identification and resolution, helping teams pinpoint the source of data quality problems and take corrective actions proactively. Additionally, with intelligent alerts and automated issue detection, DQLabs allows teams to quickly identify and resolve data quality problems before they affect analytics, reporting, and decision-making.