Fabric Performance Benchmarking - Spark versus Python Notebooks

Summary

Power BI gets insightful benchmarks that compare the performance of different data processing engines in Fabric.

Power BI and Fabric: performance benchmarking

Recently, performance benchmarks were conducted on Microsoft Fabric, comparing the data processing engines Pandas, PySpark, Polars, and DuckDB. The results show that for medium-scale datasets (up to about 100GB), modern in-process engines like DuckDB and Polars on single-node Python notebooks are consistently faster and up to 5x cheaper than distributed Spark clusters.

Why this matters

For BI professionals, choosing the right data processing engine is crucial for optimizing costs and performance. This research confirms a broader trend in the industry where simple, efficient solutions are increasingly preferred over more complex distributed systems. Competitors like Amazon Redshift and Google BigQuery need to realize that optimizing user experience and speed in data processing is key to remaining relevant in this rapidly evolving market.

Concrete takeaway

BI professionals should consider revising their data processing strategies in light of these benchmark results and explore user-friendly, modern engines like Polars and DuckDB, especially for smaller datasets.