When to transform data for Power BI.pptx
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This document discusses when to transform data for Power BI, emphasizing the choice between SQL, Power Query, and DAX based on 'Roche's Maxim,' which suggests transforming data as far upstream as possible. It outlines the benefits of early transformation, including wider data accessibility and a consistent source for reports, while also recognizing situations where downstream transformation is necessary. The document provides guidance on the appropriate use of each tool for combining data from multiple sources and encourages readers to explore further resources for detailed techniques.
When to transform data for Power BI.pptx
- 1. Power BI Tips and Timesavers When to Transform Data for Power BI? SQL or Power Query or DAX
- 2. Knowing when to transform data for your Power BI Reports can help you to decide when to use which technique SQL, Power Query or DAX This guide gives you a steer to make the right choice
- 3. When to transform data for Power BI? Over the last few weeks, we have shown how data can be combined from multiple sources We have shown that this can be done in a number of ways using three different tools But which one do you choose and why?
- 4. When to transform data for Power BI? “Data Should be transformed as far upstream as possible, and as far downstream as necessary” This is called ROCHES MAXIM, by Matthew Roche Principal Program Manager Power BI Downstream…..Closer to its end destination Upstream…..Closer to its source
- 5. Applying Roches Maxim “Data Should be transformed as far upstream as possible, and as far downstream as necessary” In simple terms, using our requirement to combine multiple data sources over the last three examples We should first look to see if we can do it in SQL using the UNION method If we can’t, then look to do it within Power Query And lastly, look to using DAX
- 6. Benefits of transforming early Transformed data can be made available for wider use cases For example, a fact table in a data warehouse that has already combined multiple data sources can be used by more users in more use cases This means that it only needs to be done once, upstream and controls in place there to ensure its complete and accurate Data is available on a consistent basis for all users You get all subsequent reports sharing a common data source and really do have…. One version of the truth
- 7. Reasons to transform downstream It's not always possible to perform the transformations upstream SQL to Power Query Data could be coming from multiple systems and not from a central data warehouse, it maybe a combination of a SQL Server database for some parts of the organisation, and a separate Oracle Database for a newly acquired part of the organisation which is being kept separate If you had the facility, you could look at creating a simple reporting database to combine these into one, but assuming that is not an option, Then you will use the next best option of Power Query to combine these sources using the APPEND functionality
- 8. Reasons to transform downstream Power Query or DAX? Generally, Power Query is better option as its further upstream as the work is done in the data model itself The data model as an entity can be shared and made available for wider use This also means that report building is less complex and easier to maintain
- 9. Reasons to transform downstream Last but not least DAX If you need to create a measure that is responsive to slicers For example You need to create a card that returns aggregated sales from multiple sources that are not already combined You can use the DAX UNION function to combine the data you need
- 10. Learn More You can learn more about Roche’s Maxim of Data Transformation on the ssbipolar.com blog Roche’s Maxim of Data Transformation – BI Polar (ssbipolar.com)
- 11. We have outlined the key reasons why you can combine data in multiple ways for Power BI When to use SQL When to use Power Query And when you need to use a DAX Measure For more details of any of these specific techniques please visit our Blog
- 12. For more Tips, Tricks and Timesavers, visit our website Business Analytics Blog – Select Distinct Credit: [email protected]