GenAI monitor recommendations in Data Profiler

We built Data Profiler to help technical and non-technical users quickly deploy effective data quality monitoring on important assets. We're now introducing recommendations powered by large language models (LLMs) that analyze data samples, table metadata, and query logs to tailor monitoring to each asset:

• Multi-column validations to monitor relationships between columns e.g. start_date should be before end_date.
• Regular expression validations to enforce arbitrary patterns and formats in data that go beyond basic checks like UUID matching or email format.
• Segmented volume and freshness to track row count or time since last row count change for segmented data such as sales by store or inventory by warehouse.