Power BI Practice: Report-Level Filters Are Ineffective for Sampling Data in Power BI
Scenario:
You are modeling data by using Microsoft Power Bl. Part of the data model is a large Microsoft SQL Server table named Order that has more than 100 million records. During the development process, you need to import a sample of the data from the Order table.
Solution: You add a report-level filter that filters based on the order date. Does this meet the goal?
No, adding a report-level filter based on the order date does not meet the goal of importing a sample of the data from the Order table during the development process.
A report-level filter only affects the data displayed in the report, not the data imported into the data model. To import a sample of the data, you should use a query-level filter in Power Query. This way, you can limit the number of records imported from the SQL Server table, which is more efficient for development purposes.
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Additional Note:
What is “Sampling data”?
Sampling data in Power BI refers to the process of selecting a subset of data from a larger dataset. This is particularly useful when working with very large datasets, as it allows you to work with a manageable amount of data during the development and testing phases. Here are a few key points about data sampling in Power BI:
[1] Efficiency: Sampling helps improve performance by reducing the amount of data that needs to be processed and visualized.
[2] Testing: It allows you to test your data models, transformations, and visualizations on a smaller dataset before applying them to the full dataset.
[3] Random Sampling: Power BI provides functions like SAMPLE in DAX, which can return a sample of rows from a table
[4] High-Density Sampling: For visualizations like scatter charts, Power BI uses algorithms to sample high-density data points to ensure that the visual remains responsive and representative