Introduction: Why Cost Optimization Matters

Power BI Premium provides dedicated capacity for large datasets, but inefficient refresh strategies can waste resources, leading to higher costs. Incremental Refresh (IR) helps reduce memory usage, processing time, and compute costs by refreshing only new and modified data instead of reloading the entire dataset. Additionally, IR reduces the load on source systems and improves network efficiency, making it a powerful cost-saving strategy.

This article explores how IR can save costs in Power BI Premium and provides real-world optimization techniques.

How Incremental Refresh Saves Costs

1. Reduces Load on Power BI Premium Capacity

• Without IR: A full refresh loads the entire dataset into memory every time, consuming more resources.
• With IR: Only recent data partitions are processed, reducing memory usage and optimizing compute resource allocation.
• Impact: More available memory means better performance for reports and reduced risk of query failures due to memory exhaustion.

Example:
A retail business tracking 5 years of sales data (100M rows) refreshes daily:

• Full refresh → Reloads 100M rows every time
• Incremental refresh (last 30 days) → Reloads only ~3M rows

Result: Less memory consumption → More datasets can run simultaneously in Premium capacity.

2. Reduces Load on Source Systems

• Without IR: A full refresh queries the entire dataset from the source system, leading to excessive resource consumption on database servers.
• With IR: Only new or modified records are queried, reducing the strain on the source system.
• Impact: Less CPU and memory usage on source systems, improving operational performance and reducing contention for resources.

Example:
A financial institution pulls 1TB of transaction data from an operational database:

• Full refresh: Heavy database load, affecting performance for other applications.
• IR with a 6-month window: Queries only the most recent data, minimizing impact.

Result: Source systems remain responsive, supporting other business-critical operations without excessive load.

3. Improves Network Efficiency

• Without IR: Transferring large volumes of data in a full refresh consumes significant network bandwidth.
• With IR: Smaller, incremental updates result in reduced data transfer over the network.
• Impact: Shorter data transfer times and lower bandwidth consumption improve overall network efficiency.

Example:
A global logistics company syncs shipping records from multiple regions:

• Full refresh → Transfers 100GB of data daily over VPN
• Incremental refresh → Transfers only 2GB of new records

Result: Network congestion is minimized, reducing latency and costs associated with data transfers.

4. Helps Avoid Scaling Up to Higher Premium SKUs

• Power BI Premium P1/P2/P3 SKUs come with fixed compute limits based on virtual cores (v-cores).
• If full refreshes consume excessive resources, organizations may be forced to upgrade to a higher SKU ($$$).
• IR prevents this by keeping refreshes lightweight, reducing the need for more expensive capacity.

Cost Breakdown Example:

• P1 SKU ($4,995/month) can support ~100GB of dataset memory.
• If full refreshes exceed this, you may need P2 SKU ($9,995/month).
• With IR, datasets remain compact, avoiding the need for a more expensive plan.

Result: Savings of ~$5,000/month by keeping data refreshes optimized with IR.

Best Practices for Maximizing Cost Savings with IR

✅ Set the Right Historical Data Retention Period – Avoid keeping unnecessary partitions.
✅ Monitor Refresh Performance – Use Power BI Metrics App to check refresh duration.
✅ Leverage Auto-Scaling in Fabric (if available) – Dynamically allocate resources only when needed.

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Conclusion: How Much Can You Save?

✅ Lower memory usage → No need for higher SKU upgrades.
✅ Faster refresh times → Frees up Premium compute for other workloads.
✅ Reduced source system load → Improves database performance and user experience.
✅ Optimized network usage → Minimizes data transfer costs and improves efficiency.

If your datasets exceed 100M rows, IR can cut costs by thousands of dollars per month while improving performance.