🚀 Motivations

As data professionals, we aim to minimize the time spent deciphering the intricacies of PySpark's framework. Often, we seek a straightforward, Pandas-style approach to compute tasks without delving into highly optimized Spark code.

To address this need, Ophelian was created with the following goals:

• Simplicity: Provide a simple and intuitive way to perform data computations, emulating the ease of Pandas.
• Efficiency: Wrap common patterns for data extraction and transformation in a single entry function that ensures Spark-optimized performance.
• Code Reduction: Significantly reduce the amount of code required by leveraging a set of Spark optimization techniques for query execution.
• Streamlined ML Pipelines: Facilitate the lifecycle of any PySpark ML pipeline by incorporating optimized methods and reducing redundant coding efforts.

By focusing on these motivations, Ophelian aims to enhance productivity and efficiency for data engineers and scientists, allowing them to concentrate on their core tasks without worrying about underlying Spark optimizations.

📝 Generalized ML Features

Ophelian focuses on creating robust and efficient machine learning (ML) pipelines, making them easily replicable and secure for various ML tasks. Key features include optimized techniques for handling data skewness, user-friendly interfaces for building custom models, and streamlined data mining pipelines with Ophelian pipeline wrappers. Additionally, it functions as an emulator of NumPy and Pandas, offering similar functionalities for a seamless user experience. Below are the detailed features:

• Framework for Building ML Pipelines: Simplified and secure methods to construct ML pipelines using PySpark, ensuring replication and robustness.
• Optimized Techniques for Data Skewness and Partitioning: Embedded strategies to address and mitigate data skewness issues, improving model performance and accuracy.
• Build Your Own Models (BYOM): User-friendly software for constructing custom models and data mining pipelines, leveraging frameworks like PySpark, Beam, Flink, PyTorch, and more, with Ophelian native wrappers for enhanced syntax flexibility and efficiency.
• NumPy and Pandas Functionality Syntax Emulation: Emulates the functions and features of NumPy and Pandas, making it intuitive and easy for users familiar with these libraries to transition and utilize similar functionalities within an ML pipeline.

These features empower users with the tools they need to handle complex ML tasks effectively, ensuring a seamless experience from data processing to model deployment.