rawY

rawY is an open-source data processing and visualization toolkit designed to handle raw data streams and unstructured sources through a declarative pipeline language and a modular execution engine. It aims to streamline data preparation, transformation, and quick visualization in a single environment, reducing the need for multiple disparate tools.

The name rawY combines the concept of raw data with a YAML-inspired configuration style, signaling an emphasis on human-readable, declarative specifications for data workflows. The project is built around the idea that complex data preparation should be reproducible, auditable, and easy to iterate, even when dealing with heterogeneous input formats.

History and development have centered on delivering a lightweight core with an extensible plugin system. Initial prototypes emerged in small research and developer communities in the early 2020s, with a public release and broader adoption following in the mid-2020s. The project maintains an open governance model, relying on community contributions and transparent roadmaps to guide feature additions such as streaming support, schema inference, and cross-language bindings.

Core features include a declarative pipeline language for composing data transformations, native support for common formats (JSON, CSV, YAML, Parquet) and streaming data, a modular plugin architecture for custom processors, and built-in visualization components. The engine emphasizes reproducibility with versioned pipelines, provenance metadata, and environment capture. It also provides bindings for popular languages and runtimes to integrate into existing data stacks.

Applications of rawY span data cleaning and ETL, exploratory data analysis, IoT and log streaming, and lightweight data science experiments, particularly in teams seeking an integrated workflow with auditable pipelines. See also YAML, data pipeline, ETL, data workflow systems.