Polars

Benchmarks for open-source Polars and Polars Nanook at 10 GB, 100 GB and 1 TB, against DuckDB, DataFusion and PySpark, with every result and the method.

Polars Nanook, formerly Polars Cloud, is a managed compute platform for running and profiling Polars queries in users’ own infrastructure.

Polars provides open-source, high-performance DataFrames for Python and Rust, installing quickly and processing data locally.

Courses that take a team from its first Polars query to production workloads.

The company raised €18 million in Series A funding for next-generation data processing.

Open roles at Polars and what it is like to work on the fastest DataFrame engine.

Polars charges no per-seat fees; users pay only for compute time, billed by the second.

Polars started with a simple goal: make data processing faster and simpler on a single machine. Today, that same mission drives the company behind it, bringing speed and simplicity to data teams of every size.

The Polars team offers direct support with guaranteed response times for bug fixes and updates.

Polars is the fastest open-source DataFrame library for Python and Rust, and the framework that takes the same query from a laptop to the cloud.

Cookie policy: the full text as it applies to Polars products and this site.

Data processing agreement “Polars Nanook”: the full text as it applies to Polars products and this site.

Privacy statement Polars: the full text as it applies to Polars products and this site.

Sub-processors “Polars Nanook”: the full text as it applies to Polars products and this site.

The legal documents that apply to Polars products and this site.

Terms of use “Polars Nanook”: the full text as it applies to Polars products and this site.