A Fine Slice Of SQL Server
The first is to connect Power BI to Azure Synapse to explore and visualize data. You can examine your datasets that you have loaded in your datalake with Power BI to help with the analysis of the data either for a data science solution or to determine how you are going to transform the data. For more information on how to do this, check out my previous blog .
Click through for three additional methods.
Comments closedGilbert Quevauvilliers heads to the photocopier:
Did you know that if you want to it is quick and easy to copy and paste multiples Power BI visuals between two open Power BI Desktop files.
Gilbert shows us exactly how to do this and even includes a GIF of the process in action.
Comments closedChris Webb does some work in Excel:
Sometimes, when you’re analysing data, you need to be able to change variables and see what the impact is: for example you might want to see what your profit margin looks like if tax rates are set at different levels. Power BI’s what-if parameter feature can do this but it has the limitation that you can’t enter any value you like – you need to create a table containing all possible variable values in advance. The advantage the Excel Data Model/Power Pivot has over Power BI for this type of what-if analysis is that you have the Excel worksheet available, which is not only a place to display your report but which can also be used as a data source for tables in the Excel Data Model, making it easy for users to enter whatever variable they want. Up until recently, though, I assumed that if you were importing data from the worksheet into the Excel Data Model you would need to take some form of manual action, for example clicking a refresh button, to load the new data from the worksheet into the Excel Data Model when the data there changed. In this blog post I’ll show you how you can use VBA to solve this problem and build an elegant what-if analysis solution in Excel where no extra clicks are needed.
Read on for that demonstration.
Comments closedChris Webb hits the turbo button:
Do you have a a large dataset in Power BI Premium or Premium Per User? Do you have more than six tables that take a significant amount of time to refresh? If so, you may be able to speed up the performance of your dataset’s refresh by increasing the number of tables that are refreshed in parallel, using a feature that was released in August 2022 but which you may have missed.
Click through for that tip.
Comments closedShabnam Watson goes investigating:
In this post, I am going to look at automatic partition maintenance by Power BI service for datasets with Incremental Refresh and focus on what happens to the partitions as time goes by. To do this, I am going to set up a couple of sample datasets with different Incremental Refresh (IR) policies with and without the Hybrid option, schedule automatic refreshes from the Power BI Service, and record how their partitions change over time. As a result, this post is going to get updated as time goes on as it documents how the partitions evolve.
Read on to learn more about what Incremental Refresh does and how things have changed over time. This looks like a post to come back to a few times.
Comments closedTeo Lachev combines two capabilities in Power BI:
In a typical engagement, I create an organizational semantic model(s) and “report packs”, such as Sales Report Pack, Inventory Report Pack, etc. These report packs are typically implemented as Power BI reports connected to the semantic model as a shared dataset using the Power BI Datasets connector. Reports sanctioned by IT are published to a dedicated workspace, such as Corporate BI. Departmental reports are deployed to their respective workspace, such as Sales, to enforce content-level security. Usually, the semantic model has row-level security (RLS) roles defined to enforce restricted access to data depending on the identity of the interactive user.
Read on to see how you can test out the results once you get it working.
Comments closedPatrick LeBlanc pulls out the thesaurus:
Most struggle with getting Q&A to be effective in Power BI. Usually this comes down to either model naming or synonyms. Patrick shows you how you can update these and also a nice feature to let you share them with others.
I’ve found the Power BI Q&A component to be a bit tetchy, even with synonyms, when you’re asking for non-trivial slices of the data. Still, what Patrick shows does help a lot.
Comments closedProbably one of the most annoying things about creating bookmarks is… updating them!
However, there is a trick that will make your life so much easier and the process a lot faster.
And the trick is…
GROUP YOUR VISUALS and always select the option “SELECTED VISUALS” when creating your bookmark
Read on to see how it works.
Comments closedReza Rad shares some thoughts with us:
DAX is the language of writing calculations in Power BI. We use DAX to write calculations such as year-over-year change and percentage, or percentage of the total or rank of customers by their yearly revenue. Writing calculations in DAX takes time, and you may likely need to re-use a calculation in multiple reports.
Creating copies of the PBIX file every time for reusing the calculation is not ideal. The better approach is to have a shared dataset created by DAX calculations and then create thin reports with live connections to the shared Power BI dataset. Using a shared dataset ensures that all the reports are using the same DAX calculations. If a change is needed, it is only needed in the shared dataset. Maintaining a solution like this would be much easier.
Click through for a dozen or so recommendations.
Comments closedMarco Russo and Alberto Ferrari ask who you believe, them or your lying eyes:
When looking at a report, it is natural to double-check the numbers produced. The simplest and most intuitive way is to verify whether the total equals the sum of individual rows. This behavior is extremely natural and mostly effective. Nonetheless, the total is the sum of rows only for additive measures, which are measures that are naturally computed as a sum.
When working with business intelligence solutions, sooner or later a developer will author a calculation that is non-additive. At that point, the total can no longer be computed by summing the rows for a very good reason: it would be inaccurate. When users complain about the fact that the rows do not sum up, seasoned BI developers offer a rational explanation of the reasons why the number are not summed: this process often provides a better understanding of how values are computed. Choosing the easy way out of introducing additivity in a naturally non-additive calculation means losing the opportunity to generate accurate calculations, and relying on inaccurate values.
Read on for examples and how to understand how to deal with non-additive or semi-additive features.
Comments closed