Category: Power BI Desktop

Azure, Business Intelligence - BI, Microsoft Fabric, Power BI, Power BI Desktop, Power BI Premium Capacity, Power BI Service

Integrating Power BI with AzureDevOps (Git), part 1: Cloud Integration


Power BI is a powerful tool for creating and sharing interactive data visualizations. But how can you collaborate with other developers on your Power BI projects and ensure quality and consistency across your reports? In this series of blog posts, I will show you how to integrate Power BI with Azure DevOps, a cloud-based software development and delivery platform. We can integrate Azure DevOps with Power BI Service (Fabric) as well as Power BI Desktop.
The current post explains how to set up Azure DevOps and connect a Power BI Workspace.
The next blog post will explain how to use it on your local machine to integrate your Power BI Desktop projects with Azure DevOps.

A brief history of source control systems

Before we dive into the details of Power BI and Azure DevOps integration, let’s take a moment to understand what source control systems are and why they are essential for any software project.

Source control systems, also known as version control systems or revision control systems, are tools that help developers manage the changes made to their code over time. They allow developers to track, compare, and roll back changes when necessary and collaborate with other developers on the same project.

There are two main types of source control systems: centralised and distributed. Centralised source control systems use Client-server approach to store all the code and its history in a single server, and developers need to connect to that server to access or modify the code. Examples of centralised source control systems are Microsoft’s Team Foundation Server (TFS) which rebranded to Azure DevOps Server in 2018, IBM’s ClearCase, and Apache’s Subversion.

On the other hand, distributed source control systems use a peer-to-peer approach, allowing each developer to have a local copy of the entire code repository, including its history. Developers can work offline and sync their changes with other developers through a remote server. Examples of distributed source control systems are Git Software and Mercurial, which takes us to the next section. Let’s see what Git is.

What is Git, and why use it?

Git is one of the world’s most popular and widely used distributed source control systems. It was created by Linus Torvalds, the creator of Linux, in 2005. Git has many advantages over centralised source control systems, such as:

  • Speed: Git is fast and efficient, performing most operations locally without network access.
  • Scalability: Git can easily handle large and complex projects, as it does not depend on a single server.
  • Flexibility: Git supports various workflows and branching strategies, allowing developers to choose how they want to organise their code and collaborate with others.
  • Security: Git uses cryptographic hashes to ensure the integrity and authenticity of the code.
  • Open-source: Git is free and open-source, meaning anyone can use it, modify it, or contribute to it.

While Git is pretty good, it has some disadvantages compared with a centralised source control system. Here are some:

  • Complexity: Git has a steep learning curve, especially for users who are new to distributed version control systems. Understanding concepts such as branching, merging, rebasing, and resolving conflicts can be challenging for beginners and sometimes even seasoned Git users.
  • Collaboration challenges: While distributed version control systems like Git enable easy collaboration, they can also lead to collaboration issues. Multiple developers working on the same branch simultaneously may encounter conflicts that need to be resolved, which can introduce complexities and require extra effort.
  • Performance with large repositories: While Git performs pretty well on most operations, it can get abortive when working with large repositories containing many files or a long history of commits. Operations such as cloning or checking out large repositories can be time-consuming.

What is Azure DevOps, and what does it relate to Git?

Azure DevOps is Microsoft’s cloud-based platform providing a set of tools and services for software development. It encompasses a range of capabilities for managing, planning, developing, testing, and delivering software applications. Azure DevOps offers:

  • Azure Boards: A tool for planning, tracking, and managing work items, such as user stories, tasks, bugs, etc.
  • Azure Repos: A tool for hosting Git repositories online, which is the main focus of this blog post.
  • Azure Pipelines: A tool for automating builds, tests, and deployments.
  • Azure Test Plans: A tool for creating and running manual and automated tests.
  • Azure Artifacts: A tool for managing packages and dependencies.

Azure DevOps also integrates with other tools and platforms, such as GitHub, Visual Studio Code, and now, Power BI. This takes us to the next section of this blog post, Integrating Power BI with Azure DevOps.

How to integrate Power BI with Azure DevOps

Now that we understand what Git and Azure DevOps are let’s see how we can integrate Power BI with Azure DevOps.

Integrating Power BI with Azure DevOps has two different integrations. Cloud integration and local machine integration have the following requirements.

Prerequisites

To follow along with this tutorial, you will need:

  • In the cloud:
    • An Azure DevOps Service
    • A Power BI account with one of the following licenses to enable Power BI Workspace integration with Azure DevOps.:
      • Power BI PPU (Premium Per User)
      • Premium Capacity
      • Embedded Capacity (EM/A)
      • Fabric Capacity
  • On your local machine:
    • The latest version of Power BI Desktop (June 2023 or later)
    • Either Visual Studio or VS Code

As stated earlier, this post explains the Cloud integration partTherefore, we require to have an Azure DevOps Service and a Power BI account with a Premium licencing plan in order to integrate Power BI with Azure DevOps.

In the following few sections, we look into more details and go through them together step-by-step.

Continue reading “Integrating Power BI with AzureDevOps (Git), part 1: Cloud Integration” →
DAX, Power BI, Power BI Desktop, Power BI Service, Power Query

Datatype Conversion in Power Query Affects Data Modeling in Power BI

Datatype Conversion in Power Query Affects Data Modeling in Power BI

In my consulting experience working with customers using Power BI, many challenges that Power BI developers face are due to negligence to data types. Here are some common challenges that are the direct or indirect results of inappropriate data types and data type conversion:

  • Getting incorrect results while all calculations in your data model are correct.
  • Poor performing data model.
  • Bloated model size.
  • Difficulties in configuring user-defined aggregations (agg awareness).
  • Difficulties in setting up incremental data refresh.
  • Getting blank visuals after the first data refresh in Power BI service.

In this blogpost, I explain the common pitfalls to prevent future challenges that can be time-consuming to identify and fix.

Background

Before we dive into the topic of this blog post, I would like to start with a bit of background. We all know that Power BI is not only a reporting tool. It is indeed a data platform supporting various aspects of business intelligence, data engineering, and data science. There are two languages we must learn to be able to work with Power BI: Power Query (M) and DAX. The purpose of the two languages is quite different. We use Power Query for data transformation and data preparation, while DAX is used for data analysis in the Tabular data model. Here is the point, the two languages in Power BI have different data types.

The most common Power BI development scenarios start with connecting to the data source(s). Power BI supports hundreds of data sources. Most data source connections happen in Power Query (the data preparation layer in a Power BI solution) unless we connect live to a semantic layer such as an SSAS instance or a Power BI dataset. Many supported data sources have their own data types, and some don’t. For instance, SQL Server has its own data types, but CSV doesn’t. When the data source has data types, the mashup engine tries to identify data types to the closest data type available in Power Query. Even though the source system has data types, the data types might not be compatible with Power Query data types. For the data sources that do not support data types, the matchup engine tries to detect the data types based on the sample data loaded into the data preview pane in the Power Query Editor window. But, there is no guarantee that the detected data types are correct. So, it is best practice to validate the detected data types anyway.

Power BI uses the Tabular model data types when it loads the data into the data model. The data types in the data model may or may not be compatible with the data types defined in Power Query. For instance, Power Query has a Binary data type, but the Tabular model does not.

The following table shows Power Query’s datatypes, their representations in the Power Query Editor’s UI, their mapping data types in the data model (DAX), and the internal data types in the xVelocity (Tabular model) engine:

Power Query and DAX (data model) data type mapping
Power Query and DAX (data model) data type mapping

As the above table shows, in Power Query’s UI, Whole Number, Decimal, Fixed Decimal and Percentage are all in type number in the Power Query engine. The type names in the Power BI UI also differ from their equivalents in the xVelocity engine. Let us dig deeper.

Continue reading “Datatype Conversion in Power Query Affects Data Modeling in Power BI” →
Power BI, Power BI Desktop, Power Query

Slowly Changing Dimension (SCD) in Power BI, Part 2, Implementing SCD 1

Slowly Changing Dimension (SCD) in Power BI, Part 2, Implementing SCD 1

I explained what SCD means in a Business Intelligence solution in my previous post. We also discussed that while we do not expect to handle SCD2 in a Power BI implementation, we can handle scenarios similar to SCD1. In this post, I explain how to do so.

Scenario

We have a retail company selling products. The company releases the list of products in Excel format, including list price and dealer price, every year. The product list is released on the first day of July when the financial year starts. We have to implement a Power BI solution that keeps the latest product data to analyse the sales transactions. The following image shows the Product list for 2013:

Products List 2013 in Excel
Products List 2013

So each year, we receive a similar Excel file to the above image. The files are stored on a SharePoint Online site.

Scenario Explained

As the previous post explains, an SCD1 always keeps the current data by updating the old data with the new data. So an ETL process reads the data from the source, identifies the existing data in the destination table, inserts the new rows to the destination, updates the existing rows, and deletes the removed rows.

Here is why our scenario is similar to SCD1, with one exception:

  • We do not actually update the data in the Excel files and do not create an ETL process to read the data from the Excel files, identify the changes and apply the changes to an intermediary Excel file
  • We must read the data from the source Excel files, keep the latest data while filtering out the old ones and load the data into the data model.

As you see, while we are taking a very different implementation approach, the results are very similar with an exception: we do not delete any rows.

Implementation

Here is what we should do to achieve the goal:

  • We get the data in Power Query Editor using the SharePoint Folder connector
  • We combite the files
  • We use the ProductNumber column to identify the duplicated products
  • We use the Reporting Date column to identify the latest dates
  • We only keep the latest rows

Getting Data from SharePoint Online Folder

As we get the data from multiple files stored on SharePoint Online, we have to use the SharePoint Folder connector. Follow these steps:

  1. Login to SharePoint Online and navigate to the site holding the Product list Excel files and copy the site URL from the browser
Getting SharePoint Online Site URL
Getting SharePoint Online Site URL
  1. From the Get Data in the Power BI Desktop, select the SharePoint Folder connector
  2. Click Connect
Connecting to SharePoint Online Folder from Power BI
Connecting to SharePoint Online Folder from Power BI
  1. Paste the Site URL copied on step 1
  2. Click OK
Connecting to SharePoint Online Folder from Power BI using the SharePoint Folder connector
Connecting to SharePoint Online Folder from Power BI using the SharePoint Folder connector
  1. Click Transform Data
Transforming data in Power Query Editor
Transforming data in Power Query Editor
Continue reading “Slowly Changing Dimension (SCD) in Power BI, Part 2, Implementing SCD 1” →