Standardizing Open Table Formats for Big Data Analysis: Implications for Machine Learning and AI Applications

Abstract

The digital age has ushered in an era of unprecedented data proliferation, both in complexity and volume, challenging traditional data management
paradigms. To address these challenges, the big data ecosystem has witnessed the rise of innovative open table formats, with Apache Parquet, Apache ORC, and Delta Lake at the forefront. These formats revolutionize data handling through advanced features like columnar storage, dynamic schema evolution, and optimized retrieval mechanisms. This paper delves into the critical need for standardizing open table formats, with a particular focus on their transformative potential in Machine Learning (ML) and Artificial Intelligence (AI) domains. We present a comprehensive comparative analysis, dissecting the features, advantages, and limitations of widely adopted open table formats. Our investigation extends to how these formats enhance the trifecta of data processing efficiency, model training effectiveness, and cross-tool data consistency in ML and AI ecosystems. The paper further explores the pivotal role of standardization in fostering interoperability, scalability, and widespread adoption of big data systems. By examining the integration capabilities across heterogeneous platforms, we highlight the far-reaching implications of standardized formats. This study aims to elucidate how the standardization of open table formats can catalyze a paradigm shift in big data analysis methodologies. Ultimately, we posit that this standardization could 
significantly accelerate innovation and enhance outcomes in the rapidly evolving landscapes of ML and AI.