If a dataflow experiences timeouts due to the number of Power Query transformations, and query optimization and refresh timing cannot be adjusted, what is the recommended action?

If a dataflow experiences timeouts due to the number of Power Query transformations, and query optimization and refresh timing cannot be adjusted, what is the recommended action?

• A. Create a second dataflow that ingests the FactSales table with no additional transformations, and then connect the original dataflow to transform the FactSales data by using this second dataflow
• B. Increase the dataflow refresh interval
• C. Remove all transformations from the primary dataflow
• D. Split the dataflow into multiple datasets with parallel execution

Answer: (A)

When a dataflow times out because there are too many Power Query transformations, the best approach is to separate ingestion from transformation by using a staging dataflow. Create a second dataflow that brings in the FactSales table with no additional transformations, then have the primary dataflow connect to that staging dataflow and perform the transformations on top of its results. This decouples the heavy processing from the initial load, reducing the workload in a single run and leveraging a dependency between dataflows to manage the transformation steps in a more controlled way. This pattern, often called a staging or hub-and-spoke design, helps because the staging dataflow handles the raw data load with minimal processing, while the consuming dataflow applies the required transformations to the already-loaded data. It improves reliability under strict timeout constraints and makes the overall refresh more manageable within the given limitations. Other options either don’t address the root cause (heavy transformation load causing timeouts) or introduce new complexities (more frequent refreshes, removing all transformations, or adding parallel datasets) without solving the timeout issue.

When a dataflow times out because there are too many Power Query transformations, the best approach is to separate ingestion from transformation by using a staging dataflow. Create a second dataflow that brings in the FactSales table with no additional transformations, then have the primary dataflow connect to that staging dataflow and perform the transformations on top of its results. This decouples the heavy processing from the initial load, reducing the workload in a single run and leveraging a dependency between dataflows to manage the transformation steps in a more controlled way.

This pattern, often called a staging or hub-and-spoke design, helps because the staging dataflow handles the raw data load with minimal processing, while the consuming dataflow applies the required transformations to the already-loaded data. It improves reliability under strict timeout constraints and makes the overall refresh more manageable within the given limitations.

Other options either don’t address the root cause (heavy transformation load causing timeouts) or introduce new complexities (more frequent refreshes, removing all transformations, or adding parallel datasets) without solving the timeout issue.