To avoid a garbage-in-garbage-out scenario when sending behavioral data to Databricks, follow these steps:

• Ensure data quality by validating and enriching raw data before processing. Snowplow's Enrich service ensures high-quality event data
• Implement data quality checks at each stage of the pipeline, including schema validation and anomaly detection
• Cleanse the data by removing irrelevant or erroneous events before pushing it into Databricks for analysis or model training
• Use monitoring tools to track data quality and take corrective actions if data issues arise