Amsterdam-based Polars, a Rust-based DataFrame library for data scientists and engineers, announced on Thursday, August 3, that it has secured $4M (approximately €3.6M) in a Seed round of funding.
The round was led by Bain Capital Ventures (BCV) with participation from individual investors.
The company says the funding will be used to expand the team and build a computing platform to run Polars efficiently at any scale.
Polars: Rust-based DataFrame library
Ritchie Vink and Chiel Peters founded Polars with the goal of bringing high-performance scientific and numeric data processing to the laptop.
“What started as a pet project of mine in 2020 has grown beyond my expectations, thanks to the open-source community,” said Vink. “Now, with the support of our investors and our community, we will focus on offering managed environments, improving cloud connectors, and supporting the many companies that already use Polars.”
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 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.
Bain Capital Ventures helps founders build businesses that transform the way we live and work.
The VC invests in B2B software startups from seed to growth across four domains — Fintech, Commerce, Apps, and Infra.
For over 20 years and with over $10B under management, BCV has helped launch and commercialise more than 400 companies, including Attentive, Bloomreach, Clari, Docusign, Flywire, LinkedIn, Moveworks, Rapid7, and Redis.
BCV has offices in San Francisco, Palo Alto, New York, and Boston.
“Polars will let data scientists and engineers focus more on their code and less on infrastructure,” says Slater Stich, Partner at BCV.
“Historically, data teams have faced a big leap in infrastructure complexity once the DataFrames they’re working with grow beyond a few gigabytes in size. Polars gives those teams a high-performance library that handles much larger data sets, even on a single node. Polars is easy to adopt for data practitioners who are already familiar with Pandas or R DataFrames,” adds Stich.