A CLI tool for processing JSON and text data with functional pipelines using Ramda, supporting both command-line and interactive browser modes.
ramda-cli is a command-line utility that enables data transformation through functional pipelines, leveraging Ramda's data-last API. It allows users to process JSON streams, text, CSV, and TSV data with concise LiveScript or JavaScript expressions, making it ideal for scripting and data manipulation tasks.
Developers and data engineers who need to manipulate structured or semi-structured data (like JSON, CSV) in Unix pipelines, especially those familiar with functional programming concepts and Ramda.
Developers choose ramda-cli for its seamless integration of Ramda's functional utilities into command-line workflows, eliminating the need to learn a new syntax while offering interactive browser mode for iterative pipeline development and support for npm module imports.
:ram: A CLI tool for processing data with functional pipelines
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Enables left-to-right composition of Ramda functions for concise data transformation, leveraging a familiar API without new syntax.
Provides a browser-based editor for building and testing pipelines with real-time feedback, speeding up development and iteration.
Supports JSON, raw text, CSV, and TSV inputs and outputs, including tables and pretty-printed JSON, via command-line flags.
Allows seamless import of any npm package into pipelines with the --import flag, extending functionality without manual setup.
Processes streaming data incrementally using transducers for efficient memory usage, ideal for large datasets.
Defaults to LiveScript, a less common language, forcing JavaScript users to add --js flag and learn LiveScript for full effectiveness.
Building large or complex pipelines in command-line arguments can become unwieldy; the --file option helps but adds extra steps.
Has a smaller community and fewer resources compared to tools like jq, which can limit support and example availability.
Being Node.js-based, it may not match the speed of native C utilities for very high-volume data processing in some scenarios.