Power BI for Embedded Analytics: An Experess Guide

How Power BI Embedded Analytics Works

TL;DR: Setting up Power BI Embedded Analytics involves creating a Microsoft Entra tenant for identity management, obtaining a Power BI Pro account to create dashboards, and selecting the embedding type based on user needs (customer-facing or internal).

Content is organized in Power BI workspaces, with authentication managed via a service principal or a master user account. To embed, set up essential parameters and integrate client-side APIs. For production, plan and configure capacity using Power BI Embedded or Premium, or Microsoft Fabric for integrated analytics across data services.

1. Create a Microsoft Entra Tenant

The first step is to create a Microsoft Entra tenant. This tenant is a reserved instance of the Microsoft Entra ID service, providing the identity management layer for your organization. It’s essentially a secure environment that’s unique to your organization and distinct from other tenants. By signing up for a Microsoft cloud service, such as Azure or Microsoft 365, you gain access to a dedicated Entra tenant, which plays a central role in managing access to Power BI and other services.

2. Power BI Pro Account

To create and edit dashboards and reports, you’ll need a Power BI Pro account. This account allows you to develop the analytics content you’ll eventually embed within your application. If you're using “Embed for your organization”, each user will also require a Power BI license.

3. Types of Power BI Embed: Embed for Your Customers vs. Embed for Your Organization

Power BI Embedded offers two main embedding options, tailored for different user scenarios:

• Power BI Embed for Your Customers (App Owns Data):In this approach, your application manages access to the data—meaning users don't need their own Power BI licenses. Instead, they can view the embedded analytics directly in your application without signing in to Power BI. This is typically used by independent software vendors (ISVs) building analytics solutions for external users, who may not have any Power BI accounts. It allows for a seamless experience for end-users, as the app “owns” the data and manages access to it.
• Power BI Embed for Your Organization (User Owns Data):This model requires each user to have a Power BI license and to sign in using their own Power BI credentials. Once logged in, users can only access content they have permissions for on Power BI’s service. “User owns data” embedding is designed for large organizations with internal-facing apps, allowing them to restrict and manage content access internally.

4. Create a Power BI Workspace and Reports

All Power BI content (reports, dashboards, and tiles) is managed within workspaces in Microsoft Fabric. To prepare for embedding, you’ll need to create and publish your analytics content to one of these workspaces. Workspaces help organize, secure, and control access to your reports and dashboards, making them easier to manage within your application. Once your content is ready, upload it to the workspace for embedding.

5. Authentication Options: Service Principal vs. Power BI Pro License (Master User)

To allow your application to securely access Power BI content, you’ll need to choose one of these authentication methods:

• Service Principal: Service principals are secure “non-human” identities in Microsoft Entra, which allow your application to authenticate to Power BI without relying on user-specific credentials. Microsoft recommends service principals for production environments because of their security benefits. You can authenticate using either an application secret or a certificate, and it’s a scalable, safe option for automated access.
• Power BI Pro License (Master User)As an alternative, you can use a master user account—a single Power BI Pro user that your application uses for authentication. While simpler to set up, this approach isn’t as secure as using a service principal. Managing a master user’s credentials requires caution, as exposing them can lead to security risks.

6. Get the Embedding Parameters and Build Your App’s Client Side

To embed your Power BI content, start by obtaining essential parameter values: Domain, Tenant ID, Client ID, Client Secret, Workspace ID, and Report ID. These parameters are crucial for establishing a secure and functional connection between your app and Power BI.

Next, create a container within your app to host the embedded report. You can use Power BI Embedded Analytics Client APIs, which offer various client-side APIs to enrich your app with advanced functionality, such as report filtering, refreshing data, and navigating between pages within a report.

7. Setting Up Capacity for Production

To move your app to production, you’ll need to configure the appropriate capacity, based on your application’s needs and target audience. Power BI offers several options:

• Power BI Embedded: Power BI Embedded is designed for ISVs and developers who want to integrate Power BI visuals into their applications. Users can consume embedded content directly within your application, without needing access to the Power BI portal. Power BI Embedded uses the A SKU, which provides flexibility and scalability tailored for embedded analytics.
• Power BI Premium: Power BI Premium is intended for enterprises seeking a comprehensive BI solution with a unified view of organizational, partner, and customer data. This service allows content consumption through mobile apps, custom-developed apps, or the Power BI portal (Power BI service). It’s suitable for both internal and external-facing applications, with the P and EM SKUs offering different levels of capacity.
• Microsoft Fabric: Microsoft Fabric is an Azure-based platform that integrates Power BI, Azure Synapse, and Azure Data Explorer in a single environment. Fabric supports embedding Power BI items using the F SKU and offers a cohesive solution for analytics across various data services.

The available capacity for Power BI embedded can be accessed through this link. 

Power BI Embedded Capacity Planning

Planning the capacity for a Power BI Embedded Analytics deployment involves several complex parameters, significantly impacting cost, scalability, and overall capacity requirements. One of the primary considerations is the structure of your data models, as this directly affects performance and resource consumption. The complexity and number of queries your application requires also play a crucial role, along with the hourly distribution of application usage and data refresh rates.

In a multi-tenant setup, these considerations become even more crucial. Managing data across multiple tenants typically involves two options: maintaining separate databases for each customer or using a single multi-tenant database.

Each approach has implications for planning the capacity.

• Separate Database for Each Customer: Create single-tenant semantic models that link directly to the respective databases. To streamline the process and avoid redundant reports with differing connection details, you can employ semantic model parameters or the Update Datasource API (templating). This ensures that the right data is retrieved efficiently without unnecessary duplication of reports.
• Single Multi-Tenant Database: Conversely, if using a single multi-tenant database, the data for each customer is differentiated through parameters in the reports. By designing reports that pull only the relevant customer data, ISVs can maintain performance while sharing a common database.
• DirectQuery vs. Data Import: The choice between DirectQuery and Data Import also impacts capacity planning. DirectQuery connects reports to data sources in real-time, reducing the size of data stored in Power BI, which can lower capacity costs. However, the user experience with DirectQuery can be less favorable. Careful consideration of these trade-offs is essential for optimizing both performance and costs.

When planning your Power BI Embed capacity, you will need to follow these steps:

1. Optimize Performance and Resource Consumption: Ensure your models are designed for efficiency.
2. Determine Minimum SKU: Assess the minimum capacity required based on anticipated loads.
3. Assess Capacity Load: Regularly evaluate how much load your capacity is handling to anticipate scaling needs.
4. Set Up Capacity Autoscale: Implement autoscaling to accommodate fluctuating demand without manual intervention.

Pros and Cons of Power BI Embedded Analytics

Like any tool, Power BI Embedded Analytics comes with its strengths and a few quirks. Here’s a quick run-through to help you weigh your options:

Pros of Power BI Embedded Analytics

• Customization and Control: You get solid control over what users see and do, from restricting access to certain data to allowing ad-hoc exploration.
• No Need for End-User Licensing: Your embedded users don’t need individual Power BI licenses, which keeps costs low and simplifies user onboarding.
• Data Security: Microsoft offers stringent security compliance options with Power BI, making sure sensitive data stays safe and sound.

Cons of Power BI Embedded Analytics

• Pricing Complexity: Power BI Embedded’s pricing can be a bit tricky to wrap your head around, and it’s based on capacity rather than individual users. Depending on your usage, costs might fluctuate more than you’d like.
• Complex Capacity Planning: Planning capacity is intricate and requires detailed knowledge of application usage patterns, query complexities, and data model structures, making it challenging to optimize resources effectively.
• Performance Trade-offs: DirectQuery may reduce costs but it’s a less favorable user experience compared to data import methods.

Power BI Embedded vs Looker Embedded vs

Tableau Embedded

Power BI Embedded offers a robust solution for integrating analytics directly into applications. Its pricing model is capacity-based rather than user-based, which can be cost-effective if managed well. However, complex capacity planning and the potential performance drawbacks of DirectQuery can be challenging.

Looker Embedded emphasizes a strong modeling layer, which enables users to create tailored reports and dashboards. However, its licensing can be intricate, potentially leading to higher costs if not carefully monitored.

Tableau Embedded excels in delivering interactive and visually appealing dashboards. Its embedding capabilities allow businesses to provide analytics directly within their applications while maintaining a familiar Tableau experience. Tableau is also known for its strong community support and extensive resources, aiding users in effectively implementing embedded analytics.

Tableau’s pricing model typically includes user-based licensing, which can lead to higher costs for organizations with many users.