{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Daft is a distributed dataframe engine for processing images, audio, video, and structured data at scale, with built-in AI operations and support for multimodal workloads.","skillfed_tags":["multimodal-data","distributed-computing","data-engine"],"use_cases":["Process e-commerce product images alongside structured metadata and run AI inference on them at scale","Load multimodal datasets from Hugging Face and apply LLM prompts or embeddings to both text and images","Scale local data processing workflows to distributed Ray clusters without rewriting code","Combine audio, video, and structured data in a single pipeline for media analysis tasks","Access and transform data from data lakes (Iceberg, Delta Lake) with native multimodal support"],"what_it_does":"Daft is a Python-native, Rust-powered distributed dataframe engine designed for AI and multimodal workloads. It processes images, audio, video, and structured data in a single framework, with built-in operations for running LLM prompts, generating embeddings, and classifying data at scale. The engine supports starting locally and scaling to distributed clusters via Ray or Kubernetes, and can access data from S3, GCS, Iceberg, Delta Lake, Hugging Face, and Unity Catalog.\n\nThe package depends on pyarrow, fsspec, tqdm, typing-extensions, and packaging. It targets developers working with mixed data types (text, images, audio, video) who need to process them together at scale without JVM overhead. The framework handles memory management and provides sensible defaults to reduce configuration burden.","worth_installing":"Yes, if you need to process multimodal data (images, audio, video, text) at scale with built-in AI operations. The active maintenance, recent releases, and lack of known vulnerabilities are positive signals. Verify the Apache 2.0 license terms match your use case, and confirm that medium install friction (compiled Rust wheels) is acceptable for your deployment environment. Not necessary if you work only with structured data or single-modality inputs."},"id":"daft","links":{"html":"https://skillfed.io/packages/daft","md":"https://skillfed.io/packages/daft.md","pypi":"https://pypi.org/project/daft/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":null,"license_treatment":"unclear","name":"daft","python_support":"supports_current","summary":"Distributed Dataframes for Multimodal Data"},"popularity":{"monthly_downloads":1118600,"position":4345,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.7.23"}