Previously you could use Power BI Helper to connect to a model in Power BI Desktop and analyze that model, getting the list of all tables, columns, measures, alongside with measure dependency and modeling advise, and also documenting everything at the end. The good news is that now with having XMLA endpointavailable, you can connect directly to Power BI datasets in the service, and get all those functionalities with that.
Read on for the full change set.
Here’s yet another entry in the list of useful things I learned from Ehren von Lehe on the Power Query MSDN forum: Table.Buffer() does not buffer nested table, record or list values inside the cells of a table.
There are some important implications to this, so check out what Chris has to say.
In my last post I mentioned the Power Query engine’s persistent cache, which in some scenarios caches the data read from a data source when a query is refreshed. Another important nugget of information that Ehren von Lehe of the Power Query dev team mentioned in a post on the Power Query MSDN forum recently is the fact that if you use File.Contents to get data from a file then the persistent cache is not used, but if you use Web.Contents to get data from the same file then the persistent cache is used. I guess the thinking here is that there is no point creating an on-disk cache containing the contents of a file that is already on disk.
Chris takes us through a couple of unexpected twists, so check it out.
One of the Power BI improvements in the March 2019 Desktop release was reduced bubble size for the Map visual. I previously wrote about the benefit of the reduction in point/bubble size. I was unaware until recently that this change made it into more than the Map visual.
The ability to reduce the point size also appears in the Format options for the Power BI Scatter chart. Previously, you could change the size option from 0 to 100 under the Shapes area. As with the Map, the Scatter now allows you to reduce the size as low as -30. I did not see this mentioned in the March Desktop blog post. I must have missed it if it was part of a previous month’s release. In any case, if you were not aware that you could set the point size from -30 to 100with the Scatter chart, now you do.
For most scenarios, I think the dot size is probably a little too big. -30 is generally too small, but I’m happy that they offer us options to get it right.
Power Query (M) made a lot of data transformation activities much easier and value replacement is one of them. You can easily right click on any desired value in Power Query, either in Excel or Power BI, or other components of Power Platform in general, and simply replace that value with any desired alternative. Replacing values based on certain conditions however, may not seem that easy at first. I’ve seen a lot of Power Query (M) developers adding new columns to accomplish that. But adding a new column is not always a good idea, especially when you can do it in a simple single step in Power Query. In this post I show you a quick and easy way to that can help you handling many different value replacement scenarios.
Imagine you have a table like below and you have a requirement to replace the values column [B] with the values of column [C] if the [A] = [B].
Click through for the solution.
In this first option, Power BI handles everything. We use the web-based Power Query Online tool for structuring the data. Power BI handles scheduling the data refresh.
The underlying data behind the dataflow is stored in a data lake. However, since it’s fully managed, this data lake is not directly accessible or visible to the customer. As with most cloud-based implementations, the infrastructure is hidden under the covers. This is what is happening if your users are utilizing dataflows currently but you haven’t specified a data lake account in the Power BI admin center.
Melissa gives us a great summary of the three patterns, so read the whole thing.
If you have a large *.pbix file, you can investigate what are the columns and tables that causing the highest storage consumption, using Power BI Helper. You can download Power BI Helper for free from here. I opened the file above in Power BI Helper, and in the Modeling Advise tab, this is what I see:
As you can see in the above output, the Date field in the Date table is the biggest column in this dataset. taking 150MB runtime memory! This is considering that we have only three distinct values in the column! Seems a bit strange, isn’t? let’s dig into the reason more in deep.
Read on for Reza’s explanation and what you can do to fix it.
The high level process is to:
1. Create a measure that returns a colour as the result
1. It can be a word, such as blue, red, green
2. It can be a hex code for a colour, like #40E0D0″, “#FFA07A”
2. Use conditional formatting and use the measure to apply the formatting on the text as a rule.
Read on for a demo.
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.