Incremental Refresh in Power BI, Part 2; Best Practice; Do NOT Publish Data Model Changes from Power BI Desktop

Incremental Refresh Best Practice, Do NOT Publish Changes from Power BI Desktop

In a previous post, I shared a comprehensive guide on implementing Incremental Data Refresh in Power BI Desktop. We covered essential concepts such as truncation and load versus incremental load, understanding historical and incremental ranges, and the significant benefits of adopting incremental refresh for large tables. If you missed that post, I highly recommend giving it a read to get a solid foundation on the topic.

Now, let’s dive into Part 2 of this series where we will explore tips and tricks for implementing Incremental Data Refresh in more complex scenarios. This blog follows up on the insights provided in the first part, offering a deeper understanding of how Incremental Data Refresh works in Power BI. Whether you’re a seasoned Power BI user or just getting started, this post will provide valuable information on optimising your data refresh strategies. So, let’s begin.

When we publish a Power BI solution from Power BI Desktop to Fabric Service, we upload the data model, queries, reports, and the loaded data into the data model to the cloud. In essence, the Power Query queries, the data model and the loaded data will turn to the Semantic Model and the report will be a new report connected to the semantic model with Connect Live storage mode to the semantic model. If you are not sure what Connect Live means, then check out this post where I explain the differences between Connect Live and Direct Query storage modes.

The Publish process in Power BI Desktop makes absolute sense in the majority of Power BI developments. While Power BI Desktop is the predominant development tool to implement Power BI solutions, the publishing process is still not quite up to the task, especially on more complex scenarios such as having Incremental Data Refresh configured on one or more tables. Here is why.

As explained in this post, publishing the solution into the service for the first time does not create the partitions required for the incremental refresh. The partitions will be created after the first time we refresh the semantic model from the Fabric Service. Imagine the case where we successfully refreshed the semantic model, but we need to modify the solution in Power BI Desktop and republish the changes to the service. That’s where things get more complex than expected. Whenever we republish the new version from Power BI Desktop to Fabric Service, we get a warning that the semantic model exists in the target workspace and that we want to Overwrite it with the new one. In other words, Power BI Desktop currently does not offer to apply the semantic model changes without overwriting the entire model. This means that if we move forward, as the warning message suggests, we replace the existing semantic model and the created partitions with the new one without any partitions. So the new semantic model is now in its very first stage and the partitions of the table(s) with incremental refresh are gone. Of course, the partitions will be created during the next refresh, but this is not efficient and realistically totally unacceptable in production environments. That’s why we MUST NOT use Power BI Desktop for republishing an already published semantic model to avoid overriding the already created tables’ partitions. Now that Power BI Desktop does not support more advanced publishing scenarios such as detecting the existing partitions created by the incremental refresh process, let’s discuss our other options.

Alternatives to Power BI Desktop to Publish Changes to Fabric Service

While we should not publish the changes from Power BI Desktop to the Service, we can still use it as our development tool and publish the changes using third-party tools, thanks to the External Tools support feature. The following subsections explain using two tools that I believe are the best.

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Quick Tips: Find Power BI Desktop Local Port Number with Model Explorer

Quick Tips: Find Power BI Desktop Local Port Number with Model Explorer

In March 2018, I wrote a blogpost called Four Different Ways to Find Your Power BI Desktop Local Port Number. Last week, Zoe Doughlas from Microsoft left a comment reminding me of a fifth method to get the port which encouraged me to write this quick tip. Thanks to Zoe!

As the name suggests, the blog was about finding Power BI Desktop’s local port number. If you do not have any clue what I mean by local port number, I strongly suggest reading that blog.

This blog focuses on yet another method that wasn’t available back then. Indeed, it is a new feature added to the October 2023 release of Power BI Desktop. This is a Quick Tip so let’s jump straight to the topic and learn how we find the port number (and more) in Power BI Desktop (Oct 2023 and later releases).

Prerequisites

As mentioned, this new feature was added to Power BI Desktop’s October 2023; therefore, we must install that release on our local machine. Indeed, the October 2023 release was packed with many other features, including the Model Explorer (the topic of this blog) and the ability to define calculation groups directly in Power BI Desktop. Many of these features are still in preview; hence, they require enabling.

The following few steps explain how to enable Preview Features in Power BI Desktop:

  1. Open Power BI Desktop and click Settings (the gear icon) from the right pane
  2. On the Options page, from the GLOBAL section, click the Preview features tab
  3. Enable the desired features; for this blog, we need the Model explorer and Calculation group authoring
  4. Click OK

The following image shows the above steps:

Enabling Preview Features in Power BI Desktop
Enabling Preview Features in Power BI Desktop

Depending on the selected features, you may need to restart your Power BI Desktop to allow them to enable.

Looking at the above image, some of you may ask “Soheil, are you using an older version of Power BI Desktop?” and I am glad you asked. The answer as always is “It depends”. And, this time it depends on the timing of writing this blog which is early December 2023, and the fact that Power BI Desktop November 2023 was released a couple of weeks ago, therefore, Power BI Desktop October 2023 is kind of OLD! And, YES! I installed Power BI Desktop Nov 2023 for the sake of writing this blogpost.

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Thin Reports, Report Level Measures vs Data Model Measures

Thin Reports, Report Level Measures vs Data Model Measures

The previous post explained what Thin reports are, why we should care and how we can create them. This post focuses on a more specific topic, Report Level Measures. We discuss what report-level measures are, when and why we need them and how we create them.

If you are not sure what Thin Report means, I suggest you check out my previous blog post before reading this one.

What are report level measures?

Report level measures are the measures created by the report writers within a Thin Report. Hence, the report level measures are available within the hosting Thin Report only. In other words, the report level measures are locally available within the containing report only. These measures are not written back to the underlying dataset, hence not available to any other reports.

In contrast, the data model measures, are the measures created by data modellers and appear on the dataset level and are independent from the reports.

Why and when do we need report level measures?

It is a common situation in real-world scenarios when the business requires a report urgently, but the nuts and bolts of the report are not being created on the underlying dataset yet. For instance, the business requires to present a report to the board showing year-to-date sales analysis but the year-to-date sales measure hasn’t been created in the dataset yet. The business analyst approaches the Power BI developers to add the measure, but they are under the pump to deliver some other functionalities which adding a new measure is not even in their project delivery plan. It is perhaps too late if we wait for the developers to plan for creating the required measure, go through the release process, and make it available for us in the dataset. Here is when the report level measures come to the rescue. We can simply create the missing measure in the Thin Report itself, where we can later share it with the developers to implement it as a dataset measure.

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Power BI 101, What Should I Learn?

This is the second part of my new series of Power BI posts named Power BI 101. In the previous post, I briefly discussed what Power BI is. In this post, I look into one of the most confusing parts for those who want to start learning Power BI. Many people jump straight online and look for Power BI training courses which there are plenty out there. But which one is the right training course for you? Let’s find out.

What do you want to gain from learning Power BI?

Regardless of attending paid training courses or being a self-learner, the above question is one of the most important questions you might ask yourself before going to the next steps. The answer to this question dictates the sort of training you must look for. Your answer to the preceding question can be one or none of the following:

  • I am a graduate/student looking at the job market
  • I am a business analyst and I want to know how Power BI can help you with my daily job
  • I am a database developer and I want to learn more about business intelligence and data and analytics space
  • I am a non-Microsoft Business Intelligence developer and I want to start learning more about Microsoft offerings
  • I am a system admin and I have to manage our Power BI tenant
  • I am a data scientist and I want to know how I can use Power BI
  • I am just ciourious to see what Power BI can do for me

As mentioned, your answer might not be any of the above, but, thinking about your reason(s) for learning Power BI can help you to find the best way to learn and use Power BI more efficiently. You can spend time and money taking some online courses and get even more confused. You don’t want that do you?

So, whatever reason(s) you have in mind to learn Power BI, most probably you fall into one of the following user categories:

Think about your goal(s) and what you want to achieve by learning Power BI then try to identify your user category. For instance, if you are a student thinking of joining an IT company as a data and analytics developer, then your user category is most probably a Power BI Developer or a Contributor.

To help you find out your user category let’s see what the above user categories mean.

Power BI Developers

The Power BI Developers are the beating hearts of any Power BI development project. Regardless of the project you will be involved with, you definitely require to have a certain level of knowledge of the following:

  • Data preparation/ETL processes
  • Data warehousing
  • Data modelling/Star schema
  • Data visualisation

To be a successful Power BI developer you must learn the following languages in Power BI:

  • Power Query
  • DAX

Depending on the types of projects you will be involved in, you may require to learn the following languages as well:

  • Microsoft Visual Basic (for Paginated Reports)
  • Python
  • R
  • T-SQL
  • PL/SQL

As a Power BI developer, you will write a lot of Power Query and DAX expressions. Most probably you require to learn T-SQL as well. The following resources can be pretty helpful:

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