GoKawiilAnalysis argues Pandas should be replaced by Polars and DuckDB for mid-size data
A conference talk and blog post argue that Pandas pushes users toward costly distributed systems like Spark or Snowflake long before their data actually requires that complexity. The author says most workloads that hit the 'Pandas cliff' around tens of gigabytes can instead be handled efficiently on a single machine using newer tools such as Polars and DuckDB, up to roughly the 100GB mark. Citing an Amazon Redshift fleet study, the piece suggests genuinely 'Big Data' scale is far rarer than commonly assumed.
GoKawiil's interpretation of the reporting above, not reported fact.
Many teams overinvest in expensive distributed infrastructure simply because Pandas becomes slow or memory-constrained, not because their data truly demands it. If single-machine tools like Polars and DuckDB can handle the vast majority of real-world workloads, organizations could cut costs and complexity significantly. This challenges the default industry assumption that scaling data problems always means adopting distributed systems.
- Pandas' inefficiencies often push users into unnecessarily complex distributed systems.
- Polars and DuckDB can handle workloads up to roughly 100GB on a single machine.
- Data from Amazon's Redshift fleet suggests true 'Big Data' scale is less common than assumed.
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Published there as: “Pandas Should Go Extinct”
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