Integrating with Azure Monitor and Azure Log Analytics to create alerts based on metrics that would indicate failures in data quality checks or anomaly detection

Developing a suite of Databricks notebooks that generate synthetic data with known quality issues and anomalies, verifying the pipeline‘s ability to detect and flag these

Using Azure Data Factory to orchestrate data movement and trigger Databricks jobs, which include data quality tests, before and after deploying updates

Leveraging MLflow within Azure Databricks to version control data quality and anomaly detection models, running automated tests against a validation dataset