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Direct Lake Vs Direct Query vs Import Mode in Power BI

Published on 3 January 2025
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Anish Arjun
Anish Arjun
Associate Consultant

Anish Arjun is an Associate Consultant with extensive experience in SAP Analytics Cloud, Power BI, and SQL. His specialization in planning, predictive analytics, and finance solutions is complemented by his proficiency in machine learning with Python. Anish excels in crafting advanced dashboards that translate complex data into actionable insights, ensuring impactful and innovative results through collaboration and strategic execution.

Businesses today rely heavily on analytics platforms to derive actionable insights and drive decision-making. Power BI, Microsoft’s flagship business intelligence tool, empowers organizations to connect, transform, and visualize data in ways that foster collaboration and agility. However, the success of any analytics initiative depends not just on the tool itself but also on how effectively it integrates with data. Power BI offers three distinct connection modes—Direct Lake, Direct Query, and Import Mode—that can significantly influence performance, scalability, and usability. Each mode is tailored to specific use cases, offering unique advantages and trade-offs. Understanding these distinctions is essential for business analysts, data engineers, and decision-makers seeking to align their analytics strategy with organizational goals. This blog explores these connection modes, comparing their features, use cases, and how to choose the right one for your needs.

In Power BI, Direct Query and Live Connection enable real-time data access without importing data into the tool. Direct Query connects to various relational databases, sending queries to the source with each user interaction. This ensures data freshness, but the performance may be impacted by query complexity and the load on the source system.  

On the other hand, Live Connection, primarily used with SQL Server Analysis Services or Azure Analysis Services, queries the model in real-time. However, all data transformations must be handled within the source model, which can optimize performance for well-structured models.

Both methods provide up-to-date data, with Direct Query offering broader database compatibility, while Live Connection is optimized for Analysis Services models. 

For the purpose of the blog, we will treat Direct Query and Live Connection as similar approaches, both working for the same type of data ingestion.

A diagram of a software process

Licensing Requirements

When selecting the appropriate connection mode in Power BI, it’s also important to consider the licensing requirements, as they can significantly impact both performance and cost. Power BI offers different licensing tiers for Direct Lake, with each tier designed to cater to varying data sizes and organizational needs. The pricing structure is as follows:

Service/Capacity SKU Max Model Size Memory Licensing Type Price (Monthly)
F2 10 GB 3 GB Fabric $168
F64 200 GB 128 GB Fabric $8,256
F2048 400 GB 4 TB Fabric $264,192
P1 (Premium) 100 GB 25 GB Premium $4,995
P5 (Premium) 400 GB 400 GB Premium Custom pricing

For further information please visit the following link: Direct Lake

A diagram of a diagram of a cloud

Licensing Transition

When considering the appropriate licensing for Direct Lake, it's essential to be aware of the current and upcoming changes in Microsoft’s Power BI offerings. For new customers, Direct Lake is supported exclusively on Microsoft Fabric (F) SKUs. While existing customers can continue using Direct Lake with Premium (P) SKUs, Microsoft recommends transitioning to a Fabric capacity SKU for optimal performance and scalability. This shift to Fabric SKUs ensures better integration with Power BI’s expanding capabilities, providing enhanced data processing and model management.

For more details on Power BI pricing, please visit the Power BI Pricing page.

Key Considerations

No. Consideration Data Import Mode Direct Lake Direct Query
1 Data Size and Storage Constrained by Power BI's storage capacity Manages larger datasets effectively Manages larger datasets effectively
2 Real-Time Requirements Does not provide real-time data Provides real-time data, suitable for dynamic reporting Provides real-time data, suitable for dynamic reporting
3 Performance Needs Best query performance due to in-memory processing Good query performance, better than Direct Query Lower query performance due to live query execution
4 Infrastructure and Resources Requires less complex infrastructure and resource management Requires more complex infrastructure and resource management Requires moderate infrastructure and resource management
5 Data Preparation and Transformation Allows comprehensive data transformations within Power BI May require initial transformations Depends on the existing database schema
6 Licensing Standard Power BI licensing May require additional licensing for data lake integration Standard Power BI licensing
7 Limitations on Data Source Limited to imported data Suitable for data lakes Limited to relational databases
8 Type of Connection In-memory connection (Import Mode) Live connection to data lake (Direct/Live) Live connection to relational database (Hybrid)
9 Advantages
  • Fastest response times
  • Access to full features
  • Offline capability
  • Real-time data
  • Scalable for large datasets
  • Low latency
  • Real-time data
  • No storage limits
  • Minimal preparation needed
10 Disadvantages
  • Requires periodic data refresh
  • Limited by storage capacity
  • Complex management
  • Resource-intensive
  • Potentially slower performance
  • Dependent on source database availability
11 Best Use Cases
  • Performance-critical scenarios
  • Manageable data sizes
  • Advanced analytics
  • Immediate data updates
  • Scalable datasets
  • Low latency situations
  • Large datasets needing real-time access
  • Acceptable data latency situations
  • Minimizing data duplication

Competitive Positioning: Power BI vs. Tableau, Looker, and SAC

Power BI, Tableau, Looker, and SAP Analytics Cloud (SAC) each offer unique advantages depending on the organization's needs. Power BI stands out for its integration with the Microsoft ecosystem, affordability, and scalability, making it ideal for organizations already using Microsoft tools. In contrast, Tableau excels in data visualization and ease of use but can be more expensive and complex when scaling for large datasets. Looker is highly effective for data exploration and governance but may require a more complex setup. SAC, best suited for SAP-centric organizations, provides strong planning and predictive analytics but can be cost-prohibitive for others. Overall, Power BI offers a balanced approach, offering real-time data access, scalability, and ease of use at a competitive price point.

Conclusion

Choosing the right connection mode in Power BI is crucial for optimizing your analytics strategy and ensuring your organization can meet its data needs effectively. Whether you're focused on real-time data, managing large datasets, or balancing licensing costs, understanding the distinctions between Data Import Mode, Direct Lake, and Direct Query will help you make an informed decision. For a tailored solution and expert guidance, reach out to our team today. We’ll work with you to assess your unique requirements and ensure Power BI delivers the insights and performance your business needs.

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