Deployment Pipelines - Support Incremental Refresh with Detect Data Changes

[tctrout]

We are leveraging Power BI models configured with Incremental Refresh and Detect Data Changes (IRDDC), using monthly partitions and a polling expression to track the maximum 'last update' date via a refreshBookmark. While Deployment Pipelines streamline model promotion, they currently lack the ability to control partition settings or manage the refreshBookmark during deployment.

We request the addition of deployment options that allow us to:

• Preserve or overwrite the refreshBookmark based on deployment context.
• Exclude partition metadata from deployments when necessary to avoid unintended refresh behavior.
• Strategically update semantic models without disrupting incremental refresh logic.
  
  

Existing 3rd Party Tools such as ALM Toolkit and TE3 provide the following options to achieve this:

These capabilities are essential for maintaining data integrity and operational efficiency in enterprise environments using IRDDC. While tools like ALM Toolkit and Tabular Editor offer workarounds, native support in Deployment Pipelines would significantly enhance governance and automation.