Hugging Face is currently the most popular AI community globally, fostering innovation and collaboration among data scientists and businesses in various aspects such as models, datasets, and applications. Hugging Face hosts a diverse range of datasets that serve not only as samples for learning and practice but also as foundational sources for enterprise model data.
In order to maximize the utility of these datasets, it is often necessary to cleanse the raw data within them and archive it in a data lake to provide a unified access point. Databend is designed with ETL/ELT-centric workflows in mind, allowing for immediate cleansing, transformation, and merging of data upon loading. It supports a wealth of structured and semi-structured data types, enabling direct querying and analysis of raw data in multiple formats. The enterprise edition further supports advanced features such as virtual columns and computed columns, ensuring that the data is always ready for use.
In this article, we will demonstrate how to effortlessly access datasets hosted on Hugging Face using Databend and perform simple yet efficient analysis and processing with SQL. Additionally, the examples in this post include a white-box model implemented in SQL, showcasing how to conduct category predictions within the data warehouse and validate the model's accuracy.