Daft is a high-performance data engine designed to simplify and accelerate the processing of multimodal data—such as text, images, audio, and video—at any scale. Built with a Rust-powered core and offering both SQL and Python DataFrame interfaces, Daft enables seamless data engineering, analytics, and machine learning workflows from local development to large-scale distributed environments. Its unified framework eliminates the need for multiple specialized tools, providing a consistent and efficient experience for handling diverse data types. Key Features and Functionality: - Unified Multimodal Processing: Natively supports structured and unstructured data, allowing users to process tables, text, images, and embeddings within a single framework. - Rust-Powered Performance: Delivers exceptional speed and efficiency through vectorized execution and non-blocking I/O, outperforming traditional data processing frameworks. - Seamless Scaling: Facilitates effortless scaling from local machines to distributed clusters without code modifications, ensuring consistent performance across different environments. - Python-Native Interface: Designed with Python at its core, Daft integrates smoothly with popular Python libraries like PyTorch and NumPy, streamlining machine learning and AI workflows. - Minimal Operations: Reduces operational overhead with built-in scaling, orchestration, logging, and model execution control, eliminating the need for infrastructure management. Primary Value and User Solutions: Daft addresses the complexities of processing diverse and large-scale datasets by providing a unified, efficient, and scalable solution. It empowers data engineers, analysts, and machine learning practitioners to build and deploy AI pipelines without the burden of managing infrastructure or integrating multiple tools. By offering a consistent API for various data modalities and automating operational tasks, Daft enhances productivity, accelerates development cycles, and enables users to focus on deriving insights and building models rather than handling data processing intricacies.