Amsterdam-based Polars, a Rust-based DataFrame library for data scientists and engineers, has secured €18M in growth funding, led by Accel, with participation from BCV, which followed up on its Seed investment.
Since its seed round, where the company raised €3.6M in 2023, the platform has grown from 250,000 to over 23 million monthly users.
The company has also launched Polars Cloud, a data platform designed to bring low-latency, distributed data processing directly integrated into the Polars environment.
Fund utilisation
The Dutch company plans to use the funds to make its open-source software fully streaming, ensuring single-node queries use hardware to maximum capacity.
The company will use the capital to develop its distributed engine, which can run all Polars queries both in the cloud and on-premises. This will deliver a single intuitive DataFrame API for all scales.
The fund will also help the Dutch company to build a one-stop data platform that provides the best Polars experience available. This includes managed hardware, autoscaling, query insights, profiling and much more.
Polars: Rust-based dataframe library
Ritchie Vink and Chiel Peters founded Polars to bring high-performance scientific and numeric data processing to the laptop.
“When I started Polars, the goal was to build a faster DataFrame library that could replace pandas and improve on many of the footguns I had experienced. Soon after, that expanded into building a state-of-the-art single-node query engine specialised for DataFrames,” says the company.
The platform empowers data scientists and engineers to analyse large DataFrames without having to set up and maintain a distributed computing cluster.
According to the company’s claims, it is one of the fastest DataFrame libraries in existence and one of the fastest-growing data processing projects on GitHub.
The Polars OSS is MIT-licensed, and the company will continue to sponsor and accelerate the open-source development of the company.
With the introduction of Polars Cloud, the focus shifts to larger capabilities. Users do not need to rewrite DataFrame APIs based on their processing scale. Instead, Polars offers a single API that remains efficient across different scales.
According to the company, the tool is intended to minimise the necessity for alternatives like PySpark, as Polars strives to eliminate that need. Code written once can scale according to users’ requirements, with just a remote call needed.
