Qlik vs Power BI: Performance Benchmarks¶

Audience: BI architects, platform engineers, capacity planners Purpose: Data-driven performance comparison to inform migration architecture decisions Reading time: 12-15 minutes

Methodology¶

Benchmarks in this document are based on representative workloads tested on comparable infrastructure. Where industry-published data is available, it is cited. Where not, benchmarks are based on internal testing with CSA-in-a-Box reference datasets.

Test environment:

• Qlik: Qlik Sense Enterprise on Windows, 4-node cluster (8-core, 64 GB RAM per node), QVD files on local SSD
• Power BI: Power BI Premium P1 (8 v-cores, 25 GB memory), Fabric F64 (64 CUs)
• Data volumes: 10M, 50M, 100M, 500M, 1B row fact tables with 5 dimension tables

Performance varies significantly based on data model design, expression complexity, hardware, network conditions, and configuration. These benchmarks provide directional guidance -- always test with your specific workload before making capacity decisions.

1. Data model capacity¶

1.1 Maximum model size¶

1.2 Compression efficiency¶

Power BI's VertiPaq engine typically achieves better compression than Qlik's in-memory engine:

Better compression means:

• More data fits in the same capacity tier
• Less memory pressure under concurrent load
• Faster query performance (more data fits in cache)

2. Query and render performance¶

2.1 Dashboard render time (initial load)¶

Time to fully render a dashboard with 8-12 visuals after opening:

2.2 Filter/selection interaction time¶

Time for all visuals to update after applying a filter:

Qlik's associative engine provides near-instant "smart search" across all fields in the model. Power BI does not replicate this search-across-everything behavior. For search-driven exploration, use Power BI Q&A (natural language) or add search-enabled slicers to reports.

2.3 Complex measure evaluation¶

Time to evaluate a complex DAX measure / Qlik expression:

3. Concurrent user performance¶

3.1 Scale testing results¶

Number of concurrent interactive users before degradation (> 5 sec render time):

3.2 Scaling approach¶

4. Data refresh / reload performance¶

4.1 Full reload / refresh comparison¶

With Direct Lake on CSA-in-a-Box, the concept of a data refresh disappears. Power BI reads the latest Delta files from OneLake automatically. The only "refresh" is the data pipeline (ADF + dbt) that updates the Gold layer, which is a data platform concern, not a BI concern. This eliminates an entire category of operational overhead and failure modes.

4.2 Incremental refresh / reload¶

5. Mobile performance¶

5.1 Mobile app comparison¶

5.2 Mobile render performance¶

6. Embedding performance¶

6.1 Embedded analytics comparison¶

6.2 Embedding cost comparison¶

Power BI Embedded uses capacity-based pricing (not per-user), which means the cost does not increase linearly with user count. This is a decisive advantage for applications with large numbers of embedded users.

7. Development speed benchmarks¶

7.1 Time to build typical BI artifacts¶

7.2 Copilot acceleration¶

Copilot in Power BI reduces development time for certain tasks:

8. Capacity planning recommendations¶

8.1 Fabric SKU sizing guide for Qlik migrations¶

8.2 Monitoring after migration¶

After migration, monitor capacity utilization using the Fabric Capacity Metrics app:

• CPU utilization -- target < 70% sustained (burst to 100% is normal)
• Memory utilization -- target < 80% (model eviction degrades performance)
• Query duration P95 -- track at < 5 sec for interactive reports
• Refresh success rate -- target 100% (Direct Lake eliminates this concern)
• Active users per hour -- track for capacity right-sizing

Cross-references¶

Maintainers: CSA-in-a-Box core team Last updated: 2026-04-30