Microsoft Fabric Semantic Model Modes-A Brief review

Microsoft Fabric introduces new possibilities for data connectivity and storage modes in Power BI. Choosing the right storage mode—Direct Lake, DirectQuery, Import, or Composite—can drastically affect performance, governance, scalability, and cost.

Below is a deep-dive comparison of the four modes.

1. Import Mode

Source: Microsoft learn

How it works:

Data is loaded and stored in Power BI’s in-memory engine (VertiPaq). Reports query this in-memory cache instead of hitting the data source every time.

✅ Benefits:

• Fastest query performance due to in-memory storage.

• Rich DAX capabilities with minimal query translation overhead.

• Supports full Power BI features like aggregations, AI visuals, quick measures.

• Works offline after refresh—no dependency on source availability.

⚠️ Limitations:

• Refresh required to stay up to date; data is only as fresh as last refresh.

• Large datasets may hit memory/size limits (though Fabric Premium/Fabric capacity helps).

• Refresh schedules can be complex and impact capacity.

• Can be costly for very large data due to storage and compute requirements.

📌 When to Use:

• Small to medium datasets (<10–20 GB compressed).

• Dashboards needing sub-second performance.

• Analytical workloads where data latency of minutes to hours is acceptable.

• Prototyping, departmental analytics, or POCs.

  
  

2. DirectQuery Mode

Source: Microsoft learn

How it works:

No data is stored in Power BI. Queries are translated into SQL (or equivalent) and run directly against the source database in real-time.

✅ Benefits:

• Always up to date—no refresh required.

• Suitable for very large datasets that cannot fit in-memory.

• Leverages source system security and row-level policies.

• Low storage overhead in Power BI.

⚠️ Limitations:

• Performance depends on source system—may be slower than Import.

• Limited DAX and modeling features (no complex calculated columns/tables).

• Query folding issues may lead to inefficiency.

• High concurrency may overload the source system.

📌 When to Use:

• Very large datasets where Import is not feasible.

• Scenarios requiring real-time or near-real-time reporting.

• When security policies must be enforced at source.

• Mission-critical operational dashboards (e.g., supply chain, clinical ops).

3. Direct Lake Mode (new in Microsoft Fabric)

Source: Microsoft learn

How it works:

Power BI directly queries OneLake Delta tables without needing to import or pre-aggregate data. It combines the real-time freshness of DirectQuery with the in-memory performance of Import, by leveraging Fabric’s Delta-Parquet storage.

✅ Benefits:

• Near real-time performance with no refresh required.

• Performance similar to Import (in-memory optimizations applied on the fly).

• Eliminates data duplication—no need to move/copy data into Power BI datasets.

• Scales natively with Fabric’s OneLake architecture.

• Simplifies governance—data stays centralized.

⚠️ Limitations:

• Currently limited to Fabric-managed Delta tables.

• Not all source systems can leverage Direct Lake yet (requires Fabric integration).

• Feature set is evolving—some advanced modeling scenarios may still need Import.

📌 When to Use:

• Fabric-first architectures where data resides in OneLake.

• Large-scale analytics with high freshness requirements.

• Scenarios where governance and single source of truth are priorities.

• Enterprises modernizing BI strategy around Fabric.

4. Composite Mode

Source: Microsoft learn

How it works:

Combines Import, DirectQuery, and Direct Lake within the same dataset. Tables can be configured individually, enabling hybrid performance models.

✅ Benefits:

• Flexibility to balance performance vs freshness.

• Frequently queried/aggregated data can be imported for speed.

• Transactional/volatile data can be kept in DirectQuery/Direct Lake for freshness.

• Supports Hybrid Tables (import + direct for historical vs real-time slices).

• Helps optimize capacity costs and system load.

⚠️ Limitations:

• Increased model complexity.

• Potential user confusion if different tables update at different cadences.

• Some scenarios may have query performance unpredictability.

• Requires governance discipline to avoid over-engineering.

📌 When to Use:

• Scenarios needing a mix of fresh operational data (DirectQuery/Direct Lake) and historical data (Import).

• Large models with both real-time and aggregated reporting requirements.

• Enterprise BI platforms where balancing performance, cost, and freshness is key.

🏆 Final Comparison Table

Mode	Speed	Freshness	Scale	Features Support	Best For
Import	🚀 Fastest	❌ Stale until refresh	Medium–Large	Full DAX & features	Small–medium datasets, analytical dashboards
DirectQuery	⚡ Moderate	✅ Real-time	Very Large	Limited modeling	Large/operational datasets, real-time ops
Direct Lake	🚀 Near-Import	✅ Near real-time	Very Large	Expanding quickly	Fabric-native analytics, OneLake users
Composite	⚡ Balanced	✅ Depends on setup	Very Large	Hybrid flexibility	Mixed workloads, enterprise BI strategies

✅ Key Takeaway:

• Use Import for speed and analytics.

• Use DirectQuery for real-time but source-dependent performance.

• Use Direct Lake for Fabric-native, scalable, near real-time analytics.

• Use Composite when you need the best of multiple worlds.

Watch related video on my you tube channel below:

https://youtu.be/M7uMVULJA_Y