Piperider: An Advanced Framework for Data Pipelining and Scheduling
The GitHub project, '[Piperider](https://github.com/InfuseAI/piperider)', developed by InfuseAI, is a free and open-source Python framework explicitly designed for data pipelining and scheduling. Piperider aims to simplify data pipeline construction, enabling data scientists and developers to focus more on their code than on orchestration. With the ubiquity of data in contemporary industries, Piperider's significance lies in making data processing and manipulation more accessible and manageable.
Project Overview:
'Piperider' aims to be an efficient tool that handles data pipelining and scheduling functions. Its purpose is to resolve the intricacy and time-consuming procedures associated with data pipeline construction. The target audience mainly includes developers and data scientists who want an advanced yet straightforward tool to handle pipeline tasks and scheduling more optimally.
Project Features:
The Piperider project introduces several critical features and functionalities. Key among these are artifact management, data pipelining, pipeline scheduling, and automatic adjustments. Artifact management pertains to managing your input and output using standard Python mode. The data pipelining feature allows chaining your Python functions as a pipeline, while scheduling talks about running your pipeline periodically with a crontab-like syntax. The automatic adjustment feature automatically manages transitive dependencies adjustments for you. These features are designed to make the experience of creating data pipelines as seamless as possible.
Technology Stack:
Piperider is developed in Python, the language of choice for many developers and data scientists due to its simplicity and powerful libraries. This project uses certain Python libraries like DateTime and Croniter for managing dates and times, and for interpreting crontab expressions respectively. Python's flexibility, combined with its powerful libraries and vast community, has undoubtedly given a significant boost to the success of this project.
Project Structure and Architecture:
In terms of project structure, Piperider has been organized into several modules each designated for a specific operation such as Artifacts, Pipelines, Engineering, Scheduling and etc. These components interact with each other to perform the comprehensive process associated with constructing and controlling data pipelines. In terms of architecture, it uses a simple yet robust design pattern which focuses on functionality and efficiency, making the routinization of complex functions possible.
