Disconnected Table method:
This method is more towards PowerBI modelers. Basically, the idea is to have a Field in a independent table (no relationship to other tables) as Slicer with your measure choice and then create a measure using SELECTEDVALUE function to have the measure dynamically switch referring measures based on the choice made on the slicer.
Click through for both methods.
A recent post on the Power BI community website asked if it was possible to compress a group of numbers into text that described the sequential ranges contained within the numbers. This might be a group of values such as 1, 2, 3, 4, 7, 8, 9, 12, 13: (note there are gaps) with the expected result grouping the numbers that run in a sequence together to produce text like “1-4, 7-9, 12-13”. Essentially to identify gaps when creating the text. This seemed like an interesting challenge and here is how I solved it using DAX.
Read on for the solution, which is conceptually very similar to the T-SQL solution but a bit different in implementation.
In this video, Christian Wade joined Adam Saxton to discuss Incremental Refresh with Power BI Premium. You can use Incremental Refresh with Power BI Premium to take your dataset beyond 1GB and avoid failures such as timeouts.
Check out the video.
There’s just one problem. I’m using the Web.Contents() function in several queries to get the JSON data from the NHL API.
Actually, the function is not the problem. It’s the way I’m using it to dynamically construct URLs so that I can iterate through lists of players or teams or whatever to retrieve the data I want.
Clearly that technique works just fine because I have data. However, when I try to schedule the refresh in the Power BI service, I get the dreaded error: “You can’t schedule refresh for this dataset because one or more sources currently don’t support refresh.”
I explained how to solve this particular problem once upon a time. For the hockey data queries, I had to use variations on that theme to get the dynamic URLs to work. To do this, I had to fix up my queries (which are actually functions) in the Query Editor by opening up the Advanced Editor and fixing the code as noted below.
Read on for her examples. Given some of the problems she ran into, it seems like it might be a good idea to pull that data into SQL Server (or somewhere) and thereby separate data retrieval from data processing.
This week at the BUILD conference, Microsoft announced that Power BI custom visuals will soon be available as charts with Excel. You’ll be able to choose a range of data within an Excel workbook, and pass those data to one of the built-in Power BI custom visuals, or one you’ve created yourself using the API.
David’s point is that you can bring in R charts, but it extends to more than that.
A Date dimension table is an essential component in most any data warehouse or reporting database so techniques to generate these tables have been around for a long time. The foundation of a Date dimension table is a table containing one row per contiguous date in a range that includes every possible transaction date or fact record. To make reporting easier, it is common practice to have multiple date dimensions in the semantic model. For example, if sales transaction facts have an Order Date and a Delivery Date, and both are used independently for reporting; there may be an Order Date dimension and a Delivery Date dimension in the model.
A common practice for building the dimension table is to just populate a single Date type column with the sequential date values. After these rows are inserted, date part functions may be used to populate additional columns by referencing the Date value in an expression. Most every language includes, for example, a MONTH() and YEAR() function to convert a date value into these date parts.
I’m hoping that Paul puts together several of these types of post, where he contrasts building something in SQL, M, and DAX so we can see which language helps most where.
As a quick follow-on from last week’s post on how to detect whether query folding is taking place when importing from OData data sources, if you’re importing data from Analysis Services you have a similar problem: how do you know whether query folding is taking place? Ensuring that query folding takes place for as many of the steps in your query – especially those that filter or otherwise reduce the amount of data returned – is very important for data refresh performance.
Although the Power Query engine generates MDX queries when importing from Analysis Services in the same way it generates SQL queries when it imports from a relational database, the View Native Query option doesn’t work for Analysis Services data sources. You can of course use a Profiler trace or xEvents to see the MDX, but for most users that will not be an option for security reasons.
Read on for a better alternative.
Using bookmarks for clearing all slicers in Power BI is not a a new function, I have been using it for many months, and advising many people to do it that way. However, I still get a lot of questions in my presentations about how to do that. That is why I ended up writing this post. This post shows you a very quick trick of having a button to clear all slicers, and the magic is all happening with bookmarks. Bookmarks store the state of a Power BI page, and can be used in many scenarios, in this post, I only show you the ability to clear all slicers in a page. To learn more about Power BI; read Power BI book from Rookie to Rock Star.
Click through for an example of how to set this up.
When you import data in a Tabular model, relationships are optimized when they are based on a single column that does not have a high number of unique values. Columns that do have a high number of unique values are known as high cardinality columns. If two tables require two or more columns to define a relationship, it is possible to create a calculated column that concatenates the values of the columns used for the relationship, on both tables. This way, the relationship is based on one single column on each side. However, the resulting calculated column will have a higher cardinality than the original columns, which is not good for performance.
Most of the time, the presence of relationships based on multiple columns suggests that a better denormalization of the model is required to obtain an optimal star schema. Nevertheless, for smaller tables or when it is not possible to apply complex transformations, concatenating columns is the preferred way to obtain the relationships required. When this happens with data loaded in memory, the only concern is the cardinality of the resulting column. However, this is not the main concern in DirectQuery mode.
A calculated column in DirectQuery translates its expression into a native SQL expression. When this is used as a JOIN condition to express the relationship in a Tabular model, this could produce a non-optimal query plan, which could introduce unnecessary slowness in the query execution. For this reason, Microsoft introduced COMBINEVALUES, which is a function expressly designed to optimize relationships based on multiple columns in DirectQuery mode.
Read on for an example.
This results in a row per ride and visualises pretty well in SSMS. If you are familiar with the geography of London you can make out the river Thames toward the centre of the image and Regents Park towards the top left:
This could be overlaid on a shape file of London or a map from another provider such as Google Maps or Mapbox.
However, when you try to load the dataset into Power BI, you find that Power BI does not natively support Geography data types. There is an idea you can vote on here to get them supported: https://ideas.powerbi.com/forums/265200-power-bi-ideas/suggestions/12257955-support-sql-server-geometry-geography-data-types-i
Hit up that idea link if you want to see geography type support within Power BI.