This post is about something new I have tried last week. The goal was to create simulated streaming data source, feed it into Power BI as a streaming dataset, create a report out of the streaming dataset, and then embed it to an web application. With proper directions provided by my teammates, I finished the implementation from end to end within 1.5 hours. I was super impressed by how awesome it is and how easy it is to implement so that I want to share those directions to you.
The source data is simulated but the process is the same with real data sets.
What about a separate Power BI Date table?
This setup is built for consistency of comparison. As people go deeper into Power BI, they typically add a separate Date table as part of a more robust data model and add relationships between tables. At the same time, they disable the default Auto Date/Time built-in hierarchies. This more advanced setup with a separate Date table allows several conveniences as well as performance and storage benefits. It’s especially true with larger models that include many facttables that each join to Date and other possible dimension tables. Tableau doesn’t currently have a comparable data model. We’ll stay conveniently away from that setup in Power BI because we only have one simple sample table.
I think both of them make this an easy operation, though Tableau is probably easier here.
The March 2019 release of Power BI Desktop has brought us keyboard accessible visual interactions. One of Power BI’s natural strengths is that you can click on a data point within a visual and have it cross-highlight or cross-filter the other visuals on a page. But keyboard-only users weren’t able to use this feature until now. This greatly raises the accessibility of the Power BI report consumption experience.
Click through to see a few of these shortcuts in action.
The article that I wrote earlier this week about the shared dimension had a lot of interest, and I’m glad it helped many of you. So I thought better to write about the basics of modeling even more. In this article, I will be focusing on a scenario that you have all faced, however, took different approaches. Is it good to have too many dimension tables? can you combine some of those tables together to build one flatten dimension table? how much should you flatten it? should you end up with one huge table including everything? In this article, I’m answering all of these questions and explaining the scenarios of combining dimensions, as usual, I explain the model in Power BI. However, the concepts are applicable to any other tools. If you like to learn more about Power BI; read Power BI book from Rookie to Rock Star.
Given how closely the ideal Power BI data model matches the Kimball model, Reza’s advice makes perfect sense.
Alberto Ferrari joins Patrick to walk through how you can use DAX to format a list of values within Power BI Desktop. This takes the concatenate values quick measure to the next level.
Transmuting Adam into Alberto shows Patrick’s ultimate power.
I frequently work on projects where we have multiple tiers on which our solution is deployed to using continuous integration / continuous deployment (CI / CD) pipelines in Azure DevOps. Once everything is deployed, you also need to monitor these different environments and check the status of the data or ETL pipelines. My tool of choice is usually Power BI desktop as it allows me to connect to e.g. SQL databases very easily. However, I always ended up creating a multiple Power BI files – one for each environment.
Having multiple files results in a lot of overhead when it comes to maintenance and also managing these files. Fortunately, I came across this little trick when I was investigating in composite models and aggregations that I am going to explain in this blog post.
I have had to do exactly this same thing, so I’m going to have to try it out myself.
As you can see, there are data labels for each subcategory (means gender and education), but no data label showing the total of each education category. for example, we want to know how much was the total sales in the High School category. Now that you know the problem, let’s see a way to fix it.
Read on for Reza’s solution to the problem. In general, if people might care about the total, do them a favor and show the total.
Let’s see how AutoML works based on what’s in the private preview (the usual disclaimer is that things will probably change). To start with, AutoML requires a dataflow (a note to Microsoft here is that AutoML will become more pervasive if it’s available in Power BI Desktop and it doesn’t require a premium capacity). In the private preview, AutoML requires the following steps. Presumably. the first (and most difficult step), preparing the dataset and cleansing the data is already done and available as a dataflow entity:
It looks like Microsoft’s taking what they learned from Azure ML and trying to port it over to Power BI.
Among all the functions in DAX; the behavior of ALL function still seems mysterious for many. Many people, don’t use it at all and end up writing a very complicated calculation for a scenario that only one simple expression can do the same job. Some people, use it, but don’t exactly know how the function works, and get some unexpected results, and call it an error. In this article, I’m going to explain what ALL function is, how it can be used, and what are use cases of using such a function in DAX and Power BI. If you like to learn more about Power BI, read the Power BI book from Rookie to Rock Star.
Read on to see how the function behaves. Reza does a good job getting into the nuance of this function.
The demand to unstacking a column into a table is not rare (see here for example: PowerBIForum ) . Also if you copy a table from a post in the Power BI community forum to the enter-data-section in Power BI, it will show up as such a one-column-table.
Note that this is different from the Entity-Attribute-Value model, as there’s no entity or attribute—it’s just values.