Monitor and manage data quality in real time

Snowplow's data pipeline is designed to ensure accurate and complete data collection by validating events against predefined schemas, separating any that encounter processing issues to prevent data corruption. Despite these measures, data quality issues can arise from incorrect tracking instrumentation or changes in tag management, often going undetected until anomalies appear in data analysis. To address this, Snowplow has introduced an enhanced toolset for proactive monitoring, allowing users to identify and diagnose data quality issues swiftly through a user interface, API connections, and email notifications. This new functionality enables Snowplow BDP customers to maintain high data quality, instilling confidence in data accuracy and completeness, and supporting broader applications of web and mobile data. The improvements are underpinned by a complete refactoring of the core pipeline technology, ensuring that errors are highly structured and distinguish real data from noise.

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