Choosing the Right Data Source for Power BI Paginated Reports

In our previous post Why Power BI Paginated Reports – Precision, Compliance, and Pixel-Perfect Reporting. Now, let’s explore how to choose the right data source — a critical decision that shapes performance, scalability, and security.

Choosing the right data source is the foundation of an effective Power BI Paginated Report. In this blog, we’ll break down the three key options – SQL sources, Power BI Semantic Models, and Power Query Connectors – through the lens of performance, scalability, and security. By aligning your source selection with reporting goals, you’ll ensure that your paginated reports are not only compliant but also optimized for reliable, real-world delivery.

How Paginated Reports Fit Any Data Landscape

One of the biggest strengths of Power BI Paginated Reports is the flexibility in sourcing data. With the latest addition of Power Query support, Paginated Reports now unlock a much wider range of connectivity options – extending beyond traditional SQL and OLAP sources to modern and cloud-based platforms. This means developers can tap into over 150 data connectors without leaving the familiar Power Query experience.

No matter where your data lives – SQL Server, Fabric, Dataverse, Oracle or even a simple Excel file – Paginated Reports can connect and deliver

Core Supported Data Sources Supported in Power BI Paginated Reports

1. Native/Direct sources:
   • Microsoft SQL Server / Azure SQL
   • Azure Synapse Analytics
   • Microsoft SQL Server / Azure Analysis Services
   • Dataverse
   • Oracle
   • Teradata
   • ODBC Sources
2. Power BI Semantic Models (Datasets)

3. Fabric SQL endpoints: Warehouse/Lakehouse SQL endpoints (treated like SQL)
4. Power Query (150+ sources unlocked) – This expands the reach of paginated reports beyond traditional SQL or OLAP systems.

Strengths and Limitations of Each Method 

When building Power BI Paginated Reports, choosing the right data connectivity method is just as important as designing the report layout itself. The choice directly impacts performance, governance, maintainability, and how well the report aligns with business needs. Let’s break down the three main options: SQL connections, Power BI Semantic Models, and Power Query.

SQL (Direct Sources) Connection

A direct SQL connection remains one of the most common methods for accessing structured relational data, often delivering higher efficiency compared to other approaches.

Strengths:

• Ideal for reports that need complex joins, aggregations, or procedural logic, which are best managed within the database engine. 
•  Allows for real-time or near real-time reporting since queries are executed directly against the live data source. 
• Typically, the fastest option for straightforward transactional queries, assuming the database is well indexed and optimized.

Limitations & Considerations:

• Logic defined in SQL is less reusable – each report may need its own query unless centralized views or stored procedures are used.
• Requires SQL skills
• Performance might decrease if you access data through an on-premises data gateway or if the queries aren’t optimized properly

Power BI Semantic Model

The Power BI Semantic Model (dataset) provides a governed, reusable, and business-friendly layer that enhances paginated reports. 

Strengths:

• Ideal when measures, KPIs, and transformations are already defined
• Business users and report authors don’t need to know SQL – they can rely on the curated, pre-modeled data
• Ensures consistency across the organization – metrics defined once are reused in both paginated and interactive reports
• Automatically inherits Row-Level Security (RLS) from the semantic model, centralizing governance and compliance

Limitations & Considerations:

• Data freshness is tied to the last scheduled dataset refresh (unless Direct Query is in use)
• DAX skills are required for advanced cases
• Performance depends on how well the dataset is designed and optimized

Power Query

Power Query offers ad-hoc and self-service reporting against diverse data sources without the need for a separate data model.

Strengths:

• Unlocks access to 150+ modern and cloud-based sources, including Excel, SharePoint, Snowflake, Salesforce, and more
• Well-suited for ad-hoc or one-off reports that don’t justify building a formal semantic model
• Ability to combine and transform data from various sources using the Power Query Online experience – a familiar interface to anyone who has used Power Query in Power BI Desktop or Excel – no SQL or DAX required

Limitations & Considerations:

• Only produce a single RDL dataset per M Query. If multiple queries exist in the Power Query editor, the dataset will be generated from the final query listed.
• Does not leverage caching or pre-aggregation – making it less suitable for high-volume or frequently used reports
• Like SQL Connections, M queries are embedded directly in the report file and lead to significant maintenance overhead
• Best practice is to push heavy transformations back to the source system whenever possible, to reduce load during report rendering

Conclusion

Choosing the right data source for your Power BI Paginated Reports ultimately depends on your reporting goals. SQL excels in speed and transactional depth. Semantic Models ensure consistency, Row-Level Security (RLS), and centralized governance. Power Query unlocks 150+ modern and cloud-based connectors, making it the most versatile choice for ad-hoc or multi-system scenarios.

By matching the method to the business requirement, you ensure your paginated reports are optimized for performance, secure by design, and scalable across the enterprise.