azureml-dataprep-rslex
Azure ML Data Preparation RustLex
Decision gist · record as of 2026-08-14
Yes, if you are already using azureml-dataprep and need faster data operations. Install it as a dependency of azureml-dataprep rather than directly, then enable Rust execution in your code. The package is actively maintained, has no known vulnerabilities, and adds no direct runtime dependencies. The main caveat is that the license treatment is unclear—verify Microsoft's terms before use in commercial or redistributed projects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- This package is not intended for direct usage—it is a backend dependency of azureml-dataprep.
- Install and enable it through the Data Prep SDK, not standalone.
- Medium install friction due to compiled wheels for multiple Python versions and platforms.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text is available. The package is marked as proprietary, so review Microsoft's licensing terms before use in commercial or redistributed contexts.
last release 2026-07-27 (18 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 488,477 downloads/mo, #6,379 on PyPI
Alternatives
Verify before relying
pip install azureml-dataprep-rslex
# Then use via azureml-dataprep SDK:
from azureml.dataprep import package_builder
package_builder.use_rust_execution(True)- Exact performance improvement over the default Data Prep execution engine is not documented in the fact sheet.
- Compatibility matrix with specific azureml-dataprep versions is not specified.
- Whether use_rust_execution(True) is available in all azureml-dataprep versions remains unclear.
What it is and what it does
Azure Machine Learning Data Prep RsLex is a Rust-based backend for the Azure ML Data Prep SDK, designed to accelerate data operations in machine learning workflows. It provides compiled implementations of data loading, transformation, and writing capabilities that the Data Prep SDK can invoke when explicitly enabled. The package itself is not meant for direct use; instead, it acts as an optional performance layer that sits behind the azureml-dataprep Python API.
Developers working with Azure ML can opt into RsLex execution by calling use_rust_execution(True) in their Data Prep code, which routes operations to this Rust implementation rather than the default Python execution path. The package is actively maintained and available for Python 3.7 through 3.13 across macOS, Windows, and Linux platforms.
Use it for
- Accelerate large-scale data loading and transformation in Azure ML training pipelines by enabling Rust execution.
- Optimize data preparation workflows that process high-volume datasets by switching to the compiled backend.
- Support cross-platform ML data pipelines on macOS, Windows, and Linux with consistent Rust-based performance.
- Reduce latency in iterative data exploration and feature engineering within Azure ML notebooks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using azureml-dataprep and need faster data operations.
Install it as a dependency of azureml-dataprep rather than directly, then enable Rust execution in your code. The package is actively maintained, has no known vulnerabilities, and adds no direct runtime dependencies. The main caveat is that the license treatment is unclear—verify Microsoft's terms before use in commercial or redistributed projects.
Install
azureml-dataprep-rslex on PyPI
Before you install
Medium install friction due to compiled wheels for multiple Python versions and platforms. Active maintenance with a recent release (18 days old). No runtime dependencies, which simplifies integration.
This package is not intended for direct usage—it is a backend dependency of azureml-dataprep. Install and enable it through the Data Prep SDK, not standalone.
License in practice
License treatment is unclear—no SPDX identifier or raw license text is available. The package is marked as proprietary, so review Microsoft's licensing terms before use in commercial or redistributed contexts.
Quickstart
pip install azureml-dataprep-rslex
# Then use via azureml-dataprep SDK:
from azureml.dataprep import package_builder
package_builder.use_rust_execution(True)Verify before relying
- Exact performance improvement over the default Data Prep execution engine is not documented in the fact sheet.
- Compatibility matrix with specific azureml-dataprep versions is not specified.
- Whether use_rust_execution(True) is available in all azureml-dataprep versions remains unclear.
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 18 days since the last release |
| First released | |
| Downloads | 488,477 / month, #6,379 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: Other/Proprietary LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: azureml_dataprep_rslex-2.26.0-cp310-cp310-macosx_10_13_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp310-cp310-manylinux1_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; azureml_dataprep_rslex-2.26.0-cp310-cp310-win_amd64.whl; azureml_dataprep_rslex-2.26.0-cp311-cp311-macosx_10_13_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp311-cp311-manylinux1_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; azureml_dataprep_rslex-2.26.0-cp311-cp311-win_amd64.whl; azureml_dataprep_rslex-2.26.0-cp312-cp312-macosx_10_13_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp312-cp312-manylinux1_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; azureml_dataprep_rslex-2.26.0-cp312-cp312-win_amd64.whl; azureml_dataprep_rslex-2.26.0-cp313-cp313-macosx_10_13_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp313-cp313-manylinux1_x86_64.whl; azureml_dataprep_rslex-2.26.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; azureml_dataprep_rslex-2.26.0-cp313-cp313-win_amd64.whl
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See also azureml-dataprep · azureml-dataprep-native · RUST · azureml-fsspec · azureml-pipeline · azureml-train · azureml-pipeline-core · azureml-dataset-runtime · azureml-defaults · azureml-contrib-services