How does the platform handle null values during data ingestion?

When handling null values during data ingestion, the platform typically operates on the principle of ignoring these values based on specific field requirements. This means that if a null value is encountered in the data being ingested, and that particular field does not have constraints that require the value (such as being mandatory), the platform will not include this null entry in the final dataset. This behavior is essential for maintaining data quality and integrity, as it ensures that only relevant and complete data is processed and integrated into the system.

By allowing null values to be ignored when appropriate, the platform can effectively manage the dataset's size and complexity, ensuring that analysts and marketers have access to valid and actionable insights without the clutter of irrelevant or incomplete data. This feature enables a more streamlined and efficient data ingestion process, aligning with best practices in data management and analysis.