Implementing a custom metrics dashboard using Azure Log Analytics to aggregate job execution times and cluster metrics for bottleneck identification

Configuring Azure Databricks to automatically adjust cluster sizes and configurations based on job execution patterns and resource utilization metrics

Leveraging Azure Monitor‘s application insights to track job execution metrics and using Azure Machine Learning for optimization opportunities identification

Using the Databricks Spark UI exclusively for job and cluster performance monitoring, with manual resource adjustments based on observations