The optimal solution for predictive scaling based on performance metrics involves:

1. Azure Machine Learning: This service analyzes historical performance metrics and workload patterns to create predictive models. These models forecast future workload patterns and resource needs for Databricks clusters.
2. Azure Function: It triggers the scaling of Databricks clusters based on predictions from Azure Machine Learning. Automating the adjustment of cluster size ensures efficient handling of variable workloads.

This approach leverages machine learning for data-driven predictions and automates scaling, optimizing resource use and cost-effectiveness for Databricks clusters.