Apache Beam vs Polars
Apache Beam
beam.apache.orgPolars
pola.rsSide by side
| Figure | Apache Beam | Polars |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · Data Transformation Tools | 9.1 (#2) | 7.7 (#20) |
| Founded | 2016 | 2023 |
| Security | Apache release files include OpenPGP signatures and SHA-512 checksums so users can verify downloaded files. | Polars says Polars Cloud compute runs in the customer's cloud environment and that customer data does not leave that environment. |
| Support | Beam users can get community support through the user mailing list, Stack Overflow, and the Beam Slack channel. | Polars Cloud offers team support for technical questions, implementation guidance, and product feedback. |
Plans
In their own words
Apache Beam
Apache Beam is an open-source programming model for building data-processing pipelines across batch and streaming workloads. The same model can support both patterns in production, while runners provide different execution back ends, including Dataflow, Flink, Spark, and the Direct Runner. SDKs and quickstarts cover Java, Python, Go, and TypeScript. Pipeline building blocks include ParDo, GroupByKey, Flatten, and Combine, alongside windowing and triggering. Beam I/O connectors supply read and write transforms for external storage in batch or streaming pipelines, with catalog examples such as BigQuery, Snowflake, Apache Parquet, and Apache Iceberg. Beam Playground provides a way to try examples and transforms without a local installation. The software is free and open source; the listed plan is free, but execution costs depend on the runner and infrastructure selected. It targets data and software developers building pipelines on supported back ends. APIs may change before the first stable 1.x release, and semantic-versioning practice has exceptions.
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.