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Category: Power BI

Timeouts in Power Query Functions

Chris Webb reminds us to look at timeouts in Power Query functions:

In the first post in this series I showed how the Power BI Service applies a limit on the total amount of time it takes to refresh a dataset in the Power BI Service, except when you initiate your refresh via an XMLA Endpoint. In this post I’ll look at the various timeouts that can be configured in Power Query functions that are used to access data.

Every time a Power BI Import mode dataset connects to a data source it goes through a Power Query query, and inside the code of that Power Query query will be an M function that connects to a specific type of data source. Most – but not all – of these M functions have the option to set timeouts. 

Read on to learn more about these timeouts, as well as other Power Query functions which have timeouts by default.

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Automating Historical Partition Processing in PBI Per User

Gilbert Quevauvilliers runs into a timing issue:

I recently had a big challenge with one of my customers where due to the sheer volume of data and network connectivity speed I was hitting the 5-hour limit for processing of data into my premium per user dataset.

My solution was to change the partitions from monthly to daily. And then once I have all the daily partitions merge them back into monthly partitions.

The challenge I had was I now had to process daily partitions from 2019-01-01 to 2021-11-30. This was a LOT of partitions and I had to find a way to automate the processing of partitions.

Not only that, but I had to ensure that I did not overload the source system too!

Read on to see what Gilbert did to solve this problem.

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Modeling Many-to-Many Relationships in Power BI

Marco Russo and Alberto Ferrari show off two techniques:

Our readers know SQLBI position regarding bidirectional relationships: they are a powerful tool that should be used with great care and avoided in most scenarios. There actually is one scenario where bidirectional relationships are useful: when you need to create a model involving a many-to-many relationship between dimensions. In this scenario, using a bidirectional filter relationship is the suggested solution. Nonetheless, there may be reasons why the bidirectional relationship cannot be created, because of ambiguity. If you face this situation, you can use a different modeling technique based on a limited many-to-many cardinality relationship, which would work even when it is set as a unidirectional relationship. The choice between the two models is not an easy one. Both come with advantages and disadvantages that need to be deeply understood in order to make the right choice.

In this article, we first provide a description of the two techniques, and then we proceed with the performance analysis of both solutions, so to provide information about which technique to use and when.

Read on for the analysis.

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Troubleshooting Timeouts with Import Refresh

Chris Webb begins a series on troubleshooting timeouts:

If you’re working with a large Power BI dataset and/or a slow data source in Import mode it can be very frustrating to run into timeout errors after you have already waited a long time for a refresh to finish. There are a number of different types of timeout that you might run into, and in this series I’ll look at a few of them and discuss some of the ways you can work around them.

In this post I’ll look at one of the most commonly-encountered timeouts: the limit on the maximum length of time an Import mode dataset refresh can take. 

Click through to see the limits and ways to (sort of) get around them.

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To and From Date Filtering in one Slicer

Reza Rad uses a date slicer:

Power BI from and to date slicer

It happens that you might have two fields as From and To date (or Start and End date) in your dataset, and you want a date slicer in the report. The date slicer has to filter records in a way that the FROM and the TO dates are in the range of dates selected in the slicer. There are multiple ways of doing this. In this article and video, I’ll explain a simple but effective method for that. I have explained in another article, how this can be done using two date slicers, you can read that from here.

Click through to see what you need and how you can put one of these in place.

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Understanding Capitalization Changes in Power BI

Jeroen ter Heerdt explains why you might see the capitalization change in text loaded into Power BI:

Some of you might have seen this before – you load some data into Power BI and suddenly the capitalization (uppercase / lowercase) of your text changes on you. Let me explain what is happening here.

Click through to learn why. Normally I’d say “and what you can do about it,” but there’s not much. I guess the best answer is, embrace consistent capitalization and then you won’t notice anything.

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Documenting Power BI Dataset Measures

Gilbert Quevauvilliers thinks about documentation:

One thing that often happens is when users are using a dataset, they want to know which measures are available. And not only that sometimes they want to know the measure definition.

This got me thinking and how best could I give this to the users in my organization to be able to find this information quickly and easily.

In the past this was a manual effort not only to export the measures, but also to maintain a document, so that as measures are added, updated, or deleted I would then need to manually update some document.

Yep, you guessed it I created a Power BI report which has got all the measures and their measure definitions, which will update with the dataset! And I show you how I did this below.

Click through to see how.sfff

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Validating Pandas DataFrames with Pydantic

Sebastian Cattes continues a series on using Pydantic:

In part 1 of the article we learned about dynamic typing, Pydantic and decorators.

In this part we will learn how to combine these concepts for Pandas DataFrame validation in our codebase.

1. Combining Decorators, Pydantic and Pandas – Combine section 2. and 3. of Part 1 to showcase how to use them for output validation.

2. Let’s define ourselves a proper spaceship!

3. Summary

Check out both parts of the article.

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