Data Reuse Pitfalls: Breaking Incremental Refresh

The incremental refresh in Power BI is crucial for efficiently updating large datasets. However, when implementing it in a data reusability context, users should be aware of potential issues to ensure smooth sailing. 

This post will explore one such pitfall and some best practices to avoid it.

Pitfall: Breaking Incremental Refresh

When enabling incremental refresh for a table using Power BI Desktop, a subtle warning pops up – the resulting published dataset won’t be downloadable. It reveals two implications that can seriously impact developers:

Managing Source Code and Access

• Storing the .pbix file for version control becomes critical, but it must live on the developer’s machine. Losing this copy makes updates tricky, leaving only the XMLA endpoint as an option that requires premium capacity.
• Other developers have restricted access to inspect or troubleshoot the dataset in Power BI Desktop without a separate .pbix backup.

Overwriting Data in Power BI Service

• Publishing from Desktop overwrites existing data in the Power BI Service without warning. For incremental datasets, this can increase refresh times significantly or hit timeout limits.
• In extreme cases, permanent data loss can occur if historical data in the source expires. Mitigating this requires ALM Toolkit for metadata-only updates, a Power BI Premium feature.

Best Practices

To avoid problems and ensure smooth sailing, consider these tips:

1. Deliberately separate the dataset from the report to prevent accidental data overwrites.
2. Carefully manage source code by backing up the .pbix file and storing it securely to maintain version control and accessibility.
3. Leverage tools like ALM Toolkit for metadata-only updates during publishing. This safeguards against data loss, albeit requiring Power BI Premium.
4. Stay informed on incremental refresh limitations and plan accordingly to empower developers to make wise decisions.

Incremental refresh stands out as a transformative tool for handling large datasets. However, developers should separate datasets and reports, exercise meticulous source code management, and employ suitable solutions to sidestep potential pitfalls. 

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