Hi @ShankarM, To set up intelligent source-to-target mapping in Databricks, start by gathering metadata for both your source and target datasets, which includes details like column names and data types. Utilize Delta Lake for efficient data management, allowing for schema evolution as your data changes. You can automate the mapping process using AI tools that suggest mappings based on historical data or by writing custom PySpark functions to handle column renaming, data type conversions, and transformations. Orchestrate the workflow with Databricks Workflows to ensure everything runs smoothly, and incorporate user feedback to continuously improve the mapping accuracy. This approach not only saves time but also enhances the reliability of your data integration processes.
