Welcome to the Daft blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
GPU Inference with @daft.cls
Run GPU models on millions of rows without OOM. Real patterns from ByteDance, Essential AI, and more.
Stateful UDFs with daft.cls: Python Classes that Scale
Turn any Python class into a distributed operator. Hold models, connections, and clients across rows with one decorator.
Stateless UDFs with daft.func - four patterns, one decorator
Row-wise, async, generator, and batch UDFs in Daft — one decorator, zero boilerplate, local or distributed.
Daft UDFs: What is a UDF and why do you need one?
Daft User Defined Functions (UDFs) let you run custom Python inside a distributed DataFrame pipeline. Leverage Row-wise, Async, Generators, and Batch.
Daft v0.7.4: Arrow-rs, OpenDAL, Flight Shuffle, and Better Metrics
Daft v0.7.4 completes its arrow-rs migration, adds Apache OpenDAL storage support, Flight shuffle for Flotilla, and a full observability stack.
Introducing daft.File: Work with Any File, Anywhere
daft.File brings lazy, distributed handling for audio, video, PDFs, and code to Daft DataFrames. One interface, local or remote.
Announcing Early Access to Daft Cloud
Early access to Daft Cloud for running model-driven AI pipelines reliably at production scale. Built on Daft OSS for continuous, resilient execution.
Prompting with DataFrames: Massively Parallel LLM Generation is Here
Discover how Daft's prompt function revolutionizes LLM workflows with massively parallel context engineering on DataFrames.
Fall 2025 Review: OSS Updates | UDFs, Functions, & daft.File
Daft Fall 2025: AI Functions, improved UDFs, faster vLLM inference, and new daft.File VideoFile subtype - plus Bigtable sink and Common Crawl loader.
