Learn the most effective way to improve slow data refresh performance in a Power BI semantic model while minimizing costs. Discover why duplicating queries or increasing capacity settings may not be the optimal solution.
Table of Contents
Question
You have a Power BI semantic model that contains four queries named Query 1, Query2. Query3, and Query4.
Query1 loads customer data into the model and is referenced by the other three queries.
You discover that data refresh for the model is slow.
You need to improve the data refresh time. The solution must minimize costs.
What should you do?
A. Run the Table.buffer function in Query1.
B. Duplicate Query1 to all the other queries.
C. Reconfigure Query1 as a dataflow entity.
D. From the Power BI Admin portal, increase the Capacity settings.
Answer
To improve the slow data refresh time in your Power BI semantic model in the most cost-effective way, the best option is C. Reconfigure Query1 as a dataflow entity.
Explanation
In the current model, Query1 loads the customer data and is referenced by the other three queries (Query2, Query3, Query4). This means that during each data refresh, Query1 runs first to load the customer data, followed by the other dependent queries. Running the same query logic multiple times is inefficient and contributes to slower refresh times.
By reconfiguring Query1 as a dataflow entity, you can separate the data ingestion process from the semantic model. The dataflow will run on a schedule to load and prepare the customer data. The other queries in your semantic model can then connect to this dataflow entity as their data source.
The key benefits of this approach are:
- The customer data is loaded only once by the dataflow, rather than multiple times by each query. This eliminates redundant data retrieval and transformation logic.
- Dataflows can be optimized for performance using techniques like incremental refresh and query folding. This allows you to load only new or changed data instead of full refreshes.
- Dataflows run on a separate compute resource, offloading the work from your semantic model’s processing. This frees up resources and improves refresh times.
The other options are not as effective:
- Option A (using Table.buffer) would buffer the customer data in memory but doesn’t address the redundant query execution.
- Option B (duplicating Query1) would worsen the performance by running even more redundant logic.
- Option D (increasing capacity settings) would add costs without addressing the underlying inefficiencies in the model design.
Therefore, reconfiguring Query1 as a dataflow entity is the most effective solution to improve data refresh performance while minimizing costs.
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