Incremental Refresh in Power BI, Part 2; Best Practice; Do NOT Publish Data Model Changes from Power BI Desktop

Incremental Refresh Best Practice, Do NOT Publish Changes from Power BI Desktop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

Continue reading “Incremental Refresh in Power BI, Part 2; Best Practice; Do NOT Publish Data Model Changes from Power BI Desktop”

Microsoft Fabric: Capacity Cost Management Part 2, Automate Pause/Resume Capacity with Azure Logic Apps

Automate Pause Resume Suspend Fabric Capacity with Azure Logic Apps

In the previous blog post, I explained Microsoft Fabric capacities, shedding light on diverse capacity options and how they influence data projects. We delved into Capacity Units (CUs), pricing nuances, and practical cost control methods, including manually scaling and pausing Fabric capacity. Now, we’re taking the next step in our Microsoft Fabric journey by exploring the possibility of automating the pause and resume process. In this blog post, we’ll unlock the secrets to seamlessly managing your Fabric Capacity with automation that helps us save time and resources while optimising the usage of data and analytics workloads.

Right off the bat, this is a rather long blog, so I added a bonus section at the end for those who are reading from the beginning to the end. With that, let’s dive in!

The Problem

As we have learned in the previous blog post, one way to manage our Fabric capacity costs is to pause the capacity while not in use and resume it again when needed. While this can help with cost management, as it is a manual process, it is prone to human error, which makes it impractical in the long run.

The Solution

A more practical solution is to automate a daily process to pause and resume our Fabric capacity automatically. This can be done by running Azure Management APIs. Depending on our expertise, there are several ways to achieve the goal, such as running APIs on running the APIs via PowerShell (scheduling the runs separately), running the APIs via CloudShell, creating a flow in Power Automate, or creating the workflow in Azure Logic Apps. I prefer the latter, so it is the method that this blog post explains.

Automating Pause and Resume Fabric Capacity with Azure Logic Apps

Here is the scenario: we are going to create an Azure Logic Apps workflow that automatically does the following:

  • Check the time of the day
  • If it is between 8 am to 4 pm:
  • Check the status of the Fabric capacity
  • If the capacity is paused, then resume it, otherwise do nothing
  • If it is after 4 pm and before 8 am:
  • Check the status of the Fabric capacity
  • If the capacity is resumed, then pause it, otherwise do nothing

Follow these steps to implement the scenario in Azure Logic Apps:

  1. Login to Azure Portal and search for “Logic App
  2. Click the Logic App service
Finding Logic Apps on Azure Portal

This navigates us to the Logic App service. If you currently have existing Logic Apps workflows, they will appear here.

Continue reading “Microsoft Fabric: Capacity Cost Management Part 2, Automate Pause/Resume Capacity with Azure Logic Apps”

Microsoft Fabric: Capacity Options and Cost Management, Part 1; The Basics

Microsoft Fabric: Capacity Options and Cost Management, Part 1

Microsoft Fabric is a SaaS platform that allows users to get, create, share, and visualise data using a wide set of tools. It provides a unified solution for all our data and analytics workloads, from data ingestion and transformation to data engineering, data science, data warehouse, real-time analytics, and data visualisation. In a previous blog post, I explained the basics of the Microsoft Fabric data platform. In a separate blog post, I explained some Microsoft Fabric terminologies and personas where I explained what Tenant and Capacities are.

In this blog post, we will explore the different types of Fabric capacities, how they affect the performance and cost of our Fabric projects, and how you can control the capacity costs by pausing the capacity in Azure when it is not in use.

Fabric capacity types

Fabric capacities are the compute resources that power all the experiences in Fabric. They are available in different sizes and prices, depending on our needs and budget. We can currently obtain Fabric capacities in one of the following options:

If we want to purchase Microsoft Fabric capacities on Azure, they come in SKUs (Stock Keeping Units) sized from F2 – F2048, representing 2 – 2048 CU (Capacity Units). A CU is a unit of measure representing the resource power available for a Fabric capacity. The higher the CU, the more resources we get on our Fabric projects. For example, an F8 capacity has 8 CUs, which means it is four times more powerful than an F2 capacity, which has 2 CUs.

When purchasing Azure SKUs with a pay-as-you-go subscription, we are billed for compute power (which is the size of the capacity we choose) and for OneLake storage, which is charged for the data stored in OneLake per gigabyte per month (approximately $0.043 (New Zealand Dollar) per GB). OneLake is the unified storage layer for all the Fabric workloads. It allows users to store and access our data in a secure, scalable and cost-effective way.

Azure Fabric capacities are priced uniquely across regions. The pay-as-you-go pricing for a Fabric capacity at Australia East region is $0.3605 (NZD) per CU per hour, which translates to a monthly price of $526.217 (NZD) for an F2 ($0.3605 * 2 * 730 hours).

Microsoft Fabric pricing overview
Microsoft Fabric pricing overview

It is important to note that billing is per second with a one-minute minimum. Therefore, we will be billed for when the capacity is not in use. Here is a full list of prices available at the Azure portal by selecting our Fabric capacity region.

Now that we have an indication of the costs of owning Microsoft Fabric capacities let’s explore the methods to control the cost.

Nuances of Fabric’s Cost of Ownership

It is important to note that all the math we have gone through in the previous section is just about the capacity itself. But are there any other costs that may apply? The answer is it depends. If we obtain any SKUs lower than F64, we must buy Power BI Pro licenses per user on top of the capacity costs. For the tiers above F64, we get unlimited free users but, BUT, we still have to purchase Power BI Pro licenses for all developers on top of the cost of the capacity itself.

Another gotcha is that the Fabric experiences are unavailable to either Power BI Premium (PPU) users or the Power BI Embedded capacities. Just be mindful of that.

The good news for organisations owning Power BI Premium capacities is that you do not need to do anything to leverage Fabric capabilities. As a matter of fact, you already own a Fabric capacity, you just need to enable it on your tenant.

Continue reading “Microsoft Fabric: Capacity Options and Cost Management, Part 1; The Basics”

Unveiling Microsoft Fabric’s Impact on Power BI Developers and Analysts

Unveiling Microsoft Fabric’s Impact on Power BI Developers and Analysts

Microsoft Fabric is a new platform designed to bring together the data and analytics features of Microsoft products like Power BI and Azure Synapse Analytics into a single SaaS product. Its goal is to provide a smooth and consistent experience for both data professionals and business users, covering everything from data entry to gaining insights. A new data platform comes with new keywords and terminologies, so to get more familiar with some new terms in Microsoft Fabric, check out this blog post.

As mentioned in one of my previous posts, Microsoft Fabric is built upon the Power BI platform; therefore we expect it to provide ease of use, strong collaboration, and wide integration capabilities. While Microsoft Fabric is getting more attention in the market, so we see more and more organisations investigating the possibilities of migrating their existing data platforms to Microsoft Fabric. But what does it mean for seasoned Power BI developers? What about Power BI professional users such as data analysts and business analysts? In this post, I endeavor to answer those questions.

I have been blogging predominantly around Microsoft Data Platforms and especially Power BI since 2013. But I have never written about the history of Power BI. I believe it makes sense to touch upon the history of Power BI to better understand the size of its user base and how introducing a new data platform that includes Power BI can affect them. A quick search on the internet provides some interesting facts about it. So let’s take a moment and talk about it.

The history of Power BI

Power BI started as a top-secret project at Microsoft in 2006 by Thierry D’Hers and Amir Netz. They wanted to make a better way to analyse data using Microsoft Excel. They called their project “Gemini” at first.

In 2009, they released PowerPivot, a free extension for Excel that supports in-memory data processing. This made it faster and easier to do calculations and create reports. PowerPivot got quickly popular among Excel users, but it had some limitations. For example, it was hard to share large Excel files with others, and it was not possible to update the data automatically.

In 2015, Microsoft combined PowerPivot with another extension called Power Query, which lets users get data from different sources and clean it up. They also added a cloud service that lets users publish and share their reports online. They called this new product Power BI, which stands for Power Business Intelligence.

In the past few years, Power BI grasped a lot of attention in the market and improved a lot to cover more use cases and business requirements from data transformation, data modelling, and data visualisation to combining all these goods with the power of AI and ML to provide predictive and prescriptive analysis.

Who are Power BI Users?

Since its birth, Power BI has become one of the most popular and powerful data analysis and data visualisation tools in the world used by a wide variety of users. In the past few years, Power BI generated many new roles in the job market, such as Power BI developer, Power BI consultant, Power BI administrator, Power BI report writer, and whatnot, as well as helping many others by making their lives easier, such as data analysts and business analysts. With Power BI, the data analysts could efficiently analyse the data and make recommendations based on their findings. Business analysts could use Power BI to focus on more practical changes resulting from their analysis of the data and show their findings to the business much quicker than before. As a result, millions of users interact with Power BI on a daily basis in many ways. So, introducing a new data platform that sort of “Swallows Power BI” may sound daunting to those whose daily job relates to content creation, maintenance, or administrating Power BI environments. For many, the fear is real. But shall the developers and analysts be afraid of Microsoft Fabric? The short answer is “Absolutely not!”. Does it change the way we used to work with Power BI? Well, it depends.

To answer these questions, we first need to know who are Power BI users and how they interact with it.

Continue reading “Unveiling Microsoft Fabric’s Impact on Power BI Developers and Analysts”