RsReduce

Rsreduce is an open-source software tool and library designed to reduce the size of large datasets and data streams while preserving analytical quality. It provides configurable reduction strategies that can be applied to tabular data, time series, and feature matrices. The project aims to balance data utility and resource efficiency, enabling faster processing and smaller storage footprints in data pipelines.

Core concepts include selectable lossiness, where users can choose to downsample, aggregate, or compress features with controllable loss, and lossless reduction, where exact data retention is preserved while removing redundancy. The tool supports pluggable strategies such as systematic sampling, stratified sampling, temporal downsampling, hierarchical aggregation, and feature hashing, depending on user needs.

Implementation and usage: Rsreduce is designed to be usable from the command line and as a library in data processing workflows. It provides a simple configuration format to specify datasets, target reduction level, and chosen strategies. It can integrate with common data formats (CSV, Parquet, JSON) and can operate on batch and streaming data. It emphasizes reproducibility through versioned configurations and deterministic sampling where possible.

Impact and ecosystem: The project emphasizes transparency and documentation, with examples and tutorials that illustrate typical reduction pipelines for machine learning, analytics, and research. It is maintained by an open community that contributes modules and backends for different storage systems and compute backends.

See also: Data reduction, downsampling, data compression, sampling, feature hashing.