Performance Benchmark: Polars vs DuckDB vs Daft vs Spark on 650GB Delta Lake Dataset
By
tanelpoder
6mo ago· 11 min readenInsight
100/100
Golden Brown
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Summary
The article presents a performance comparison benchmark of four data processing frameworks (Polars, DuckDB, Daft, and Spark) on a 650GB Delta Lake dataset stored on S3. The author discusses 'cluster fatigue' in the data engineering community, highlighting the emotional and financial costs of running SaaS Lake Houses. The benchmark evaluates these single-node frameworks as alternatives to distributed systems, examining their performance, ease of use, and suitability for modern data workloads. The article reflects on the growing interest in simpler, more cost-effective data processing solutions that can handle large datasets without complex cluster management.
Key quotes
· 4 pulledThe fact that I even sold t-shirts, tells me I have born a few acolytes into this troubled Lake House world.
Without rehashing the entire article, it's clear that there is what I would call 'cluster fatigue.' We all know it, but never talk about it ... much ... running SaaS Lake Houses is expensive emotionally and financially.
I recently tried to light the tinder for what I hoped would be a revolt — the Single Node Rebellion — but, of course, it sputtered out immediately.
Truth be told, it was one of the most popular articles I've written about in some time, purely based on the stats.
cluster fatigue
