Ferret: An Innovative Declarative Language for Web Scraping and Automation
Introducing Ferret, a groundbreaking open-source software project that fosters web scraping, data harvesting, and browser automation with its uniquely developed declarative language. Hosted on GitHub by MontFerret, this project has been designed to revolutionize the way we perceive and approach data extraction from web pages, pushing the boundaries by uniting the capabilities of traditional browser automation tools and scraping libraries.
Project Overview:
Ferret’s primary objective is to deliver a comprehensive, efficient, and user-friendly platform for mining data from web pages. The project targets developers, data analysts, and enthusiastic beginners, easing their tasks of fetching relevant data from the web. Addressing the currently existing gap for a tool that combines browser automation and scraping libraries, Ferret also allows users to write less code and retrieve data in a friendly format.
Project Features:
Key features of Ferret include its proprietary declarative language, multipurpose usability and the ability to work with modern Single Page Applications. Ferret's unique language simplifies the extraction of data from highly dynamic websites, making this a unique selling point of the project. For instance, fetching real-time stock market data or tracking airline ticket prices become a lot easier and quick-to-implement due to the architectural brilliance and feature-rich nature of Ferret.
Technology Stack:
Ferret utilizes various advanced technologies. It's built around the Go programming language, ensuring high performance and efficient resource usage. The software also incorporates Google's Chrome browser, taking advantage of Chrome's development protocols. Highlighting JavaScript's capabilities, Ferret interacts with dynamic web pages effectively. This combination of technologies guarantees a powerful and reliable solution for web scraping and automation.
Project Structure and Architecture:
The project boasts a straightforward structure. It encapsulates a core library, a set of drivers, a database, and command-line interface (CLI). The CLI is built on top of the core library and uses it for evaluating FQL scripts, while the core library does all the heavy lifting, like parsing and executing FQL scripts.
Contribution Guidelines:
Ferret encourages new contributions and fosters innovation. To ensure effective participation, it has set guidelines for ensuring quality code contributions, bug submissions, and feature requests. Contributors are advised to provide detailed documentation along with their code and follow the outlined coding standard for a harmonious development environment.
