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SPECIFICTION
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data.table vs Polars

data.table

rdatatable.gitlab.io

Polars

pola.rs

Side by side

Figuredata.tablePolars
Price fromFreeFree
Free planYesYes
Free trial—Yes
Open source——
Web—Yes
WindowsYesYes
macOSYesYes
LinuxYesYes
API—Yes
Self-hosted—Yes
Plans published11
Spec score · Data Transformation Tools8.5 (#11)7.7 (#20)
Integrationsdata.table is an R package and can use R functions from other packages in queries.The homepage lists support for CSV, JSON, Parquet, Delta Lake, AVRO, Excel, Feather, Arrow, several databases, and cloud storage.
SupportThe project directs users who need help to the data.table community on Stack Overflow.Polars Cloud offers team support for technical questions, implementation guidance, and product feedback.

Plans

data.table

  • data.tableFree

data.table pricing →

Polars

  • Polars (open source)Free

Polars pricing →

In their own words

data.table

data.table is a free R package providing a high-performance version of base R’s data.frame. Its code interface supports filtering, grouping, aggregation, ordered and rolling joins, overlapping and non-equi joins, reshaping with dcast and melt, and adding, updating, or deleting columns. fread() reads delimited text, including CSV, TSV, compressed .gz and .bz2 files, and can source web URLs or shell commands. fwrite() writes delimited files with an emphasis on speed for large files. Many operations use multiple CPU threads internally. Columns can be changed by reference without copies, and the project reports no external Imports, LinkingTo, or Depends dependencies. Queries can call R functions from other packages. data.table runs on Linux, macOS, and Windows and requires base R. It is self-hosted and licensed under MPL-2.0. The project’s stated scope excludes binary formats such as Parquet and data stored on disk or in remote SQL databases.

Visit rdatatable.gitlab.io

Polars

Polars is an open-source DataFrame library for data manipulation, designed for single-machine processing with a typed, expressive API. Its multithreaded query engine is written in Rust and uses vectorized columnar processing to execute work in parallel. A query optimizer selects an execution plan, and the streaming API can process datasets larger than memory without holding all results in memory. Installation examples cover Python, Rust, and JavaScript. Supported formats include CSV, JSON, Parquet, Delta Lake, Avro, Excel, Feather, and Arrow IPC; the homepage also lists databases and cloud storage. Polars can read and write data in AWS S3, Azure Blob Storage, and Google Cloud Storage. The open-source library costs nothing under the MIT license. Polars Cloud runs distributed queries through the Polars API and scales work from local execution to a cluster; it is available on AWS. Its 30-day trial begins when an organization connects its first workspace to AWS, and its compute runs in the customer's cloud environment. Polars On-Prem supports Kubernetes and bare-metal deployments, including air-gapped Enterprise deployments.

Visit pola.rs