Establish Power BI Deployment Pipelines: Easy Guide 101


Power BI Deployment Pipelines_FI

The term “Visual Analytics” refers to the combination of data analytics and visualizations. This problem-solving approach aims to effectively facilitate high-level, complex activities such as reasoning and data-driven decision-making by integrating interactive visual representations with underlying analytical processes. Microsoft Power BI is a BI tool and Visual Analytics engine that provides real-time high-level analytics, extensive modeling, and custom development. Through intuitive, interactive, and simple-to-use services, it has led the charge in making business analysis more efficient.

In this article, you will gain information about Power BI Deployment Pipelines. You will also gain a holistic understanding of Power BI, its key features, Deployment Pipelines, their importance in Power BI, and steps to set up Power BI Deployment Pipelines. 

Read along to find out in-depth information about Power BI Deployment Pipelines.

Table of Contents

What is Power BI?

Power BI Deployment Pipelines: Power BI Logo| Hevo Data
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Power BI is a Proprietary Data Visualization and Business Intelligence platform that is part of the Microsoft Power Platform. It is one of the most widely used business tools for analyzing data and creating reports. Power BI uses a variety of built-in software services, applications, and connectors to provide engaging visuals, interactive reports, and insights.

Users can use Power BI to aggregate, analyze, visualize, and share data. It is a SaaS-based platform with desktop, mobile, and on-premise server options. Power BI can connect to a variety of data source connectors to load data directly into the dashboard and visualize data stored in databases or data warehouses. Users use Power BI charts, graphs, KPIs, and reports to analyze data and gain interactive insights.

Power BI offers 2 services:

Power BI Desktop is a free software application that can be downloaded and installed on your computer. It is a powerful data analysis and reports creation tool that lets you connect to, transform, visualize, and analyze your data. It includes the Query Editor, which allows you to connect to and combine various data sources into a data model (also known as modeling). You can then create a report using that data model. Reports can be shared directly with others or by publishing to the Power BI service.

Power BI Service is a cloud-based or Software-as-a-Service (SaaS) solution. It enables teams and organizations to collaborate and edit reports. Connecting to data sources in the Power BI service is also possible, but modeling is limited. Power BI is used to create dashboards and apps, as well as to analyze and explore data to uncover business insights.

Key Features of Power BI

Some of the main features of Power BI are listed below.

  • Hybrid Development: Power BI integrates with a variety of connectors, allowing users to connect to a variety of data sources.
  • Modeling View: Power BI enables users to divide and slice complex data models into simpler forms, separate diagrams, and multi-select objects.
  • AI Support: Power BI supports AI in Data Analytics, which users can use to prepare data, build Machine Learning models, and quickly identify insights from structured and unstructured data.
  • Quick Insights: Power BI makes it easier for users to create data subsets and apply Data Analytics to that data.
  • Report Sharing: Users can easily share their reports with other organization users without having to worry about data security.

To have further information about Power BI, you can visit here.

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What is a Deployment Pipeline?

A Deployment Pipeline is the process of removing code from version control and making it automatically available to users of your application. When a team of developers is working on a project or feature, they need a reliable and efficient way to build, test, and deploy their work.

Power BI Deployment Pipelines allow creators to create and test content in the Power BI service before it is consumed by users. In simple terms, these Power BI Deployment Pipelines are in charge of managing the lifecycle of organizational content. Reports, paginated reports, dashboards, datasets, and dataflows are among the content types.

A Power BI Deployment Pipeline has three stages:

1) Development

This stage is used to collaborate with other creators to design, build, and upload new content. This is the initial stage of a Power BI deployment pipeline.

2) Test

After making the necessary changes, in the testing stage, you can upload the modified content in order for it to be moved to this testing stage. Some of the  examples of what can be accomplished in a testing environment are as follows:

  • Distribute content to testers and reviewers.
  • Load and run tests on larger amounts of data.
  • Test your application to see how it will appear to your customers.

3) Production

After testing the report content, you can move to the production stage. In this stage, you can share the final version of your content with the stakeholders across the organization during the production stage.

Why are Power BI Deployment Pipelines Important?

Power BI Deployment Pipelines are important because of the following reasons:

  • You can easily create and share multiple Power BI deployment pipelines.
  • Content can be easily deployed and copied across Development, Test, and Production environments.
  • You have the ability to approve or deny changes or updates made by others for the next deployment pipeline.
  • To maintain data connections, you can configure the parameter and data source rules settings for each environment.
  • You can limit the number of users in the production workspace to a bare minimum in pipeline permissions, whereas development and test can include more users.

How to Setup Power BI Deployment Pipelines?

1) Setting up Power BI Deployment Pipelines: Prerequisites

The following are the prerequisites for Power BI deployment pipelines setup:

  • Power BI Premium Account: Setting up Deployment Pipelines requires a Power BI premium account. You can have any of the following to gain access to Power BI Deployment Pipelines:
    • A Power BI Premium capacity account for the organization, with Pro, accounts for the users. For publishing reports from the Power BI Desktop, you’ll need the Pro account.
    • Power BI Premium account on a per-user basis.
  • Workspace Permissions: The Power BI Deployment Pipeline is built at the workspace level. The workspace must be a premium one, created with a premium account and you must be the administrator of that workspace. The premium workspaces will show up with a diamond as indicated below:
Power BI Deployment Pipelines: Workspace Permissions| Hevo Data
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  • Data sources: The following data sources are supported by the Power BI Deployment Pipelines feature:
    • Azure Analysis Services
    • SSAS
    • Azure Synapse
    • SQL Server
    • Azure SQL Server
    • OData feed
    • Oracle
    • Sharepoint
    • SAP Hana (only the import mode)
    • Teradata

For using other data sources, you can try using parameters, if your data sources allow.

2) Setting up Power BI Deployment Pipelines: Considerations

Here are a few considerations for you to take into account, before getting started with the Power BI Deployment Pipelines set up:

  • In the development stage, you cannot create deployment dataset rules.
  • To create a deployment rule, you must be the owner of the dataset.
  • You are not required to have all three environments. Even two will suffice. You can use the Test and Production pipelines if you only have two.
  • The feature also allows for backward deployment. So, if you already have a stable production environment, you can deploy it backward (to Test or Dev, depending on your requirements) and configure the pipeline. The feature is not restricted to any sequential orders.
  • Power BI recently released REST APIs for the Deployment Pipelines. The APIs empower you to automate the syncing process as well. The API documentation currently covers a few critical functions, and Power BI will most likely add more in the future.
  • If any data source defined in a rule is changed or removed from the source dataset, the rule becomes invalid and the deployment fails.
  • Parameter rules cannot be defined for any binary type of parameter.
  • You can define Data source rules can be defined for the data sources listed in the prerequisites section.

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  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis in a BI tool such as Power BI.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
  • Scalable Infrastructure: Hevo has in-built integrations for 100’s sources that can help you scale your data infrastructure as required.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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3) Setting up Power BI Deployment Pipelines: Steps

Now let’s quickly run through the step-by-step Deployment Pipelines setup in Power BI. For this tutorial, we’ll adapt to the stages in the following sequence – Development, Test, and Production.

  • Step 1: Log in to
  • Step 2: In the left navigation pane, scroll down and click on the “Deployment Pipelines” option.
Power BI Deployment Pipelines: Deployment Pipelines Option| Hevo Data
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  • Step 3: Now, click on the “Create a Pipeline” button. 
  • Step 4: Create a deployment pipeline” popup window appears. You can enter your credentials:
    1. Pipeline name (mandatory)
    2. Description (optional)
Power BI Deployment Pipelines: Create a Deployment Pipeline Popup| Hevo Data
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  • Step 5: Now, click on the “Create” button.
  • Step 6: Then, you can click on the “Assign a Workspace” button in order to assign a workspace for the pipeline.
Power BI Deployment Pipelines: Assign a Workspace Option| Hevo Data
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  • Step 7: “Assign the workspace to a deployment stage” window appears. Choose the appropriate workspace from the dropdown (only premium workspaces will show up in the dropdown).
  • Step 8: Now, select the deployment stage for the workspace. Click on the “Assignbutton.
Power BI Deployment Pipelines: Assign the workspace to a deployment stage| Hevo Data
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  • Step 9: Your development environment has been configured in Power BI. The card displays the total number of Dataflows, Datasets, Reports, and Dashboards in the environment. Now, you can click on the “Deploy to Test” button.
Power BI Deployment Pipelines: Deploy to Test button| Hevo Data
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  • Step 10: Your development workspace’s dataset and content have now been copied to the Test Workspace. As shown in the image below, Power BI automatically created a new premium workspace with the name YourDevelopmentWorkspaceName [Test].
Power BI Deployment Pipelines: Green tick| Hevo Data
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The green tick between the Development and Test workspaces indicates that they are in sync with one another.

  • Step 11: You can rename the workspace using the Workspace Settings option, as shown below, based on your organization’s naming convention standards.
Power BI Deployment Pipelines: Workspace Settings| Hevo Data
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  • Step 12: Since the datasets in the Test workspace are deployed directly from the Development environment, they are still linked to the Dev data sources. You can follow the steps below to point the datasets to the data sources in your Test environment.
    1. Click the lightning icon in the top right corner of your test card.
Power BI Deployment Pipelines: Lightning Icon| Hevo Data
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  1. Now, update the Data Source and Parameter rules for each of the Datasets displayed.
Power BI Deployment Pipelines: Datasets| Hevo Data
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  1. Now click on the “Add Rule” button for your Data Source or Parameter as needed, and click the “Save” button.
Power BI Deployment Pipelines: Add Rule| Hevo Data
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  1. The green tick must remain after updating the data source and parameter rules to indicate that the dataset and contents are in sync between the two environments (even though they are connected to the data sources in different environments).
Power BI Deployment Pipelines: Deploy to Production| Hevo Data
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  • Step 13: To deploy your test content to the production environment, click the “Deploy to Production” button and then repeat steps 10–12 on the Production card. After successfully deploying and changing the parameter and data source rules, the screen will look like as shown below, with a green tick between your test and production cards.
Power BI Deployment Pipelines: Green tick| Hevo Data
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You have now successfully deployed the Pipeline in Power BI from the ground up.


In this article, you have learned about Power BI Deployment Pipelines. This article also provided information on Power BI, its key features, Deployment Pipelines, their importance in Power BI, and steps to set up Power BI Deployment Pipelines.

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.

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Hevo Data with its strong integration with 100+ Data Sources (including 40+ Free Sources) such as Power BI allows you to not only export data from your desired data sources & load it to the destination of your choice but also transform & enrich your data to make it analysis-ready. Hevo also allows integrating data from non-native sources using Hevo’s in-built REST API & Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

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Share your experience of understanding the Power BI Deployment Pipelines in the comment section below! We would love to hear your thoughts.

Manisha Jena
Research Analyst, Hevo Data

Manisha Jena is a data analyst with over three years of experience in the data industry and is well-versed with advanced data tools such as Snowflake, Looker Studio, and Google BigQuery. She is an alumna of NIT Rourkela and excels in extracting critical insights from complex databases and enhancing data visualization through comprehensive dashboards. Manisha has authored over a hundred articles on diverse topics related to data engineering, and loves breaking down complex topics to help data practitioners solve their doubts related to data engineering.

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