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How to Spot (and Fix) 5 Common Performance Bottlenecks in Pandas Workflows

Slow data loads, memory-intensive joins, and long-running operations—these are problems every Python practitioner has faced. They waste valuable time and make iterating on your ideas harder than it should be. This post walks through five common pandas bottlenecks, how to recognize them, and some workarounds you can try on CPU with a few tweaks to your code—plus a GPU-powered drop-in accelerator, cudf.pandas, that delivers order-of-magnitude speedups with no code changes. Don’t have a GPU on yo

Topics: cudf df gpu memory pandas

Sirius: A GPU-native SQL engine

Sirius is a GPU-native SQL engine. It plugs into existing databases such as DuckDB via the standard Substrait query format, requiring no query rewrites or major system changes. Sirius currently supports DuckDB and Doris (coming soon), other systems marked with * are on our roadmap. Performance Running TPC-H on SF=100, Sirius achieves ~10x speedup over existing CPU query engines at the same hardware rental cost, making it well-suited for interactive analytics, financial workloads, and ETL jobs.