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Category: Power BI

Against Publishing Power BI Model Changes from PBI Desktop

Soheil Bakhshi has some thoughts:

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.

Read on for plenty of detail, including your available options and how to use them.

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Fabric F2 Performance

Teo Lachev has started a new series. We begin with warehouse ETL:

As inspired by Amir Netz‘s encouragement to partners to test the Fabric F2 capacity performance, I got on a quest to test what it would do to ETL loads for Fabric Warehouse. I must admit that I was skeptical that a quarter of a core would take a warehouse off the ground, but as usual, life proved me wrong and “wrong” is a big understatement of what happened.

After provisioning a Fabric F2 capacity and a warehouse, I settled on the Retail Data Model for World Wide Importers sample star schema dataset consisting of five dimension tables and one fact table. In terms of performance, I was mostly interested in how long it would take for the ADF copy activity to insert all the data (50 million rows) in the fact table. Granted, it’s a limited test but enough to rule out the technology for real-life projects. Then, I compared the performance against Azure SQL Database Serverless running on up to 2 cores and provisioned by the free trial offer that Microsoft has on Azure. To exclude impact on data transfer between regions, both technologies were provisioned on East US 2 data region, which is the region where my Power BI tenant is hosted on.

Then we have report load time:

What a better way to spend a lazy holiday afternoon than to do more Fabric performance testing? In my previous post, I shared my results from a single-threaded ETL load test to gauge the F2 ingest performance and F2 did pretty well (or at least outperformed Azure SQL DB). Will F2 hold as parallelism increases? Throughput testing is especially important for report loads because parallel tasks can run within a report, such as visuals executing DAX queries in parallel, and across reports, such as when concurrent report requests overlap.

I’m legitimately surprised at the results. I expected F2 to be barely sufficient for testing purposes. Read both posts to see how it performs and some caveats around performance.

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Building Nested Data Types in Excel

Chris Webb shows off a feature:

A year ago support for nested data types in Excel was announced on the Excel blog, but the announcement didn’t have much detail about what nested data types are and the docs are quite vague too. I was recently asked how to create a nested data type and while it turns out to be quite easy, I thought it would be good to write a short post showing how to do it.

I came into this prepared to cringe, but it’s actually pretty cool insamuch as you’re using Excel as a UI for business work. And, let’s be honest, Excel is still the most common UI for business work out there.

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Power BI, Event Streaming, and Notebooks in Microsoft Fabric

Tomaz Kastrun continues a series on Microsoft Fabric. Day 18 has us looking at Power BI:

We have created a Power BI report directly from the datalake and today we will check how to do same with dashboard and paginated reports.

Day 19 covers event streaming:

In Fabric, you can create streaming semantic model and when selecting you will get the usual sources:

Day 20 shows how you can work with notebooks in Microsoft Fabric:

Notebooks have been around for a long time and people, community, and professionals have proven the usability, practicality, versioning and reliability of notebooks. Not to mention the clarity and hygiene. But opinions are also divided.

The purpose of this post today is to check for a couple of functionalities that might not be that straightforward when it comes to notebooks.

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The Updated Stacked Bar Chart in Power BI

Tom Martens reviews an updated visual:

Personally, the stacked bar chart holds a special place in my heart when it comes to data visualization. It’s the tool I find myself using most frequently, which is why I decided to share a template using Deneb that I’ve been utilizing for a considerable amount of time: https://www.minceddata.info/2023/11/12/the-better-rectangular-pie-chart/

With the December 2023 release of Power BI Desktop, I can almost create the Deneb visual, which is fantastic as it eliminates the need for an additional custom visual. It’s important to note that while I’m a huge fan of Deneb, I also serve as the Power BI/Fabric sherpa in a large organization, and for this, I always try to reduce overall system complexity.

Click through for a fairly complex example of the visual.

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Generating Fabric Delta Tables from Power BI Semantic Models

Nikola Ilic is excited:

A few days ago, while preparing materials for the customer training on Microsoft Fabric, I stumbled upon a very interesting article at Microsoft Learn. The article describes how to integrate Power BI semantic models (aka datasets) into OneLake.

At first glance, this doesn’t sound like something epic, but when I started thinking more and more about it, I realized that this really might be a huge thing in many different scenarios. First of all, at the moment of writing, this feature is still in preview – this means, it can change to some extent in the coming months, before eventually becoming GA. Nevertheless, I decided to take a shot and explore what can be done with OneLake integration for semantic models.

Read on to learn more about what this is doing and what you can do with it.

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Warehousing and Power BI in Microsoft Fabric

Tomaz Kastrun continues a series on Microsoft Fabric. Day 15 covers building a warehouse:

I have named my as “Advent2023_DWH”.

You can create a warehouse using T-SQL scripts, from data flow gen2, from data pipelines and from the sample data. Let’s select the sample data and grab a coffee.

Day 16 looks at data pipelines:

With the Fabric warehouse created and explored, let’s see, how we can use pipelines to get the data into Fabric warehouse.

In the existing data warehouse, we will introduce new data. By clicking “new data”, two options will be available; pipelines and dataflows. Select the pipelines and give it a name.

And Day 17 provides a primer on how Power BI can read Fabric assets:

Within the Power BI in Fabric, you will find many of the components, that can be used to create a final report. And here are the components:

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Delta Table Incremental Refresh in Power BI

Chris Webb shows off a bit of functionality:

One of the coolest features in Fabric is Direct Lake mode, which allows you to build Power BI reports directly on top of Delta tables in your data lake without having to wait for a semantic model to refresh. However not everyone is ready for Fabric yet so there’s also a lot of interest in the new DeltaLake.Table M function which allows Power Query (in semantic models or dataflows) to read data from Delta tables. If you currently have a serving layer – for example Synapse Serverless or Databricks SQL Warehouse – in between your existing lake house and your import mode Power BI semantic models then this new function could allow you to remove it, to reduce complexity and cut costs. This will only be a good idea, though, if refresh performance isn’t impacted and incremental refresh can be made to work well.

Click through to learn more about the performance of this operation and how it all works.

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Data Modeling for Sankey Charts in Power BI

Marco Russo and Alberto Ferrari explain when Sankey charts can actually make sense:

Picture this: you manage a company that sells subscription services on the web, and you want to track the evolution of your customers by analyzing different events to understand how many customers start a trial before they purchase or how many renew or cancel their subscriptions.

The report should look like this: the darker flow indicates the number of customers who skipped the trial and went directly from a phone call to purchasing a subscription.

Read on for that sales funnel example and how you can prepare the data to make best use of Power BI’s Sankey chart visual.

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