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.
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.
Daft v0.7.5: A Plugin System, 5x Faster Parquet, and a Real-Time Query Debugger
Native Extensions via Stable C ABI, Live Query Dashboard, and 2-5x faster Parquet Reads on Nested Types
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.
How We're Making Observability Better in Daft
Daft Observability Roadmap: metrics, OTEL integration, real-time dashboards, and DataFrame APIs for debugging and monitoring distributed pipelines.
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.
Daft v0.7.3: OTEL for Flotilla, Nightly Builds, and Lance NN Search
Daft v0.7.3 adds distributed observability with df.metrics via OTEL, nightly builds, and native Lance vector search.
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.
