Power BI is one of the most powerful reporting platforms out there. But as datasets grow, even well-designed reports can start taking minutes to render — something no business user enjoys.
I recently worked on optimizing a large-scale Power BI report built on a dataset with billions of rows. Initially, the report took 2–3 minutes to load. After a series of targeted optimizations, I brought that down to just a few seconds. In this article, I’ll share what I did, why it works, and the results.
🚦The Challenge
Large dataset with billions of rows.
Fact table joins with multiple dimensions.
Key visuals with DISTINCTCOUNT (conversation count, correlation count, request ID count) were extremely slow.
Report load time: 2–3 minutes (even after scaling from F32 to F64 capacity in Fabric).
Clearly, throwing more compute at the problem wasn’t the solution.
🛠️ The Optimizations
1️⃣Partitioning the Fact Table
Fact tables often grow very large. Without partitioning, every query has to scan the entire dataset, even if a filter (like date) is applied.
I partitioned the fact table based on commonly used dimension keys (especially date key).
This allowed Power BI to scan only the relevant partitions, not the entire dataset.
💡Why it matters: Partitioning improves performance because query engines can “prune” partitions and read less data. With billions of rows, this pruning drastically reduces scan time.
2️⃣Converting High-Cardinality Strings into Hashed Integers
Some of the slowest visuals were based on DISTINCTCOUNT over string columns (e.g., conversation ID, request ID).
I created hashed integer values for these string columns.
Visuals then performed DISTINCTCOUNT over integers instead of strings.
💡Why it matters:
Strings are expensive to compare—long values, more memory, more CPU.
Integers are lightweight and optimized for comparison, sorting, and counting.
Hashing reduced query complexity, making DISTINCTCOUNT operations lightning fast.
This was the game changer. Visuals that took 60–90 seconds started rendering in a couple of seconds.
3️⃣Optimizing Data Location (Reducing Latency)
Initially, data was stored in ADLS Gen2 (West US), while Fabric capacity was in Central US. This cross-region setup added latency.
I moved the data into OneLake in Central US, closer to Fabric.
💡Why it matters: Data proximity reduces network latency, making queries slightly faster. While not as impactful as partitioning or hashing, every millisecond counts.
4️⃣Smarter Capacity Utilization
Originally, scaling up from F32 to F64 Fabric capacity didn’t fix the performance issues. But after optimizations:
Reports ran smoothly even on F32 capacity.
This meant not only better performance but also lower cost per user.
💡Why it matters: True optimization isn’t about scaling up hardware—it’s about making your queries smarter and lighter.
🚀The Impact
Report load time reduced from 2–3 minutes → under 10 seconds.
Key visuals (DISTINCTCOUNT over high-cardinality columns) dropped from 30–40 seconds → 2–3 seconds.
Reduced compute from F64 → F32, cutting costs while improving performance.
End users now enjoy a smooth, interactive reporting experience.
🎯Key Takeaways for Power BI Engineers
Partition large fact tables by commonly used filters (e.g., DateKey).
Convert strings to hashed integers to speed up DISTINCTCOUNT.
Co-locate data and compute to avoid cross-region latency.
Optimize before scaling up—performance tuning often saves both time and cost.
⚡Power BI is only as fast as the design of your data model. By applying these optimizations, you can deliver enterprise-scale reports that are not only cost-efficient but also truly interactive.
💬Have you tried similar approaches in your Power BI projects? I’d love to hear about other techniques that worked for you!
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