Polars versus pandas in 2025: the real-world practice

Polars runs 3 to 10 times faster than pandas on aggregations, joins, and filters over parquet datasets of 1 to 20 GB, and holds 40 to 60 percent less memory thanks to native Arrow columns. With the 1.x API frozen and Arrow interoperability, both libraries can coexist in one pipeline.

June 17, 2025 6 min 359 4.3

Artificial Intelligence

DataFrames and Pipelines in Spark: Data Processing Optimisation

Spark DataFrames are distributed, schema-based tables that the Catalyst engine optimises automatically, while pipelines chain those transformations into a reproducible end-to-end flow. Together they let you process large data volumes efficiently across a cluster, scaling from a laptop to hundreds of nodes without rewriting code.

March 24, 2023 5 min 226 4.4