Snowflake has introduced a native data quality solution called Data Quality Monitoring, which utilizes Data Metric Functions (DMFs) to enforce data validation within Snowflake tables and columns. These DMFs allow users to define custom rules for data validation that are seamlessly integrated into Snowflake, offering a cost-effective and secure alternative to third-party tools by minimizing external access to sensitive data. The system supports real-time validation, triggering checks immediately upon data changes, unlike fixed schedules of third-party solutions. Despite lacking some advanced features found in external tools, such as machine learning-based anomaly detection, Snowflake's DMFs offer significant benefits for organizations extensively using Snowflake. Users can define specific validation rules, attach them to tables, and schedule them for immediate execution upon data updates, with results easily accessible in standard formats. Snowflake also supports setting up email notifications for validation errors, ensuring timely awareness of data issues. As the process of setting up DMFs can be labor-intensive for large datasets, automation solutions like Foundational's Free DMF Generator can help streamline the creation and maintenance of these functions, making data validation more efficient and manageable.