Cutting dead frames from DROID: 500 hours of robot data in 32 seconds
The robot state recorded next to the video tells you which frames matter before you decode any pixels. Trimming all of DROID takes 32 seconds on a laptop.
Turning robot video into training-ready data with daft-physical-ai
A Python library built on Daft for turning robot video into training-ready data, starting with hand tracking and reward scoring as UDFs, with more to come.
How we made our LeRobot video reader up to 15× faster
LeRobot has emerged as the dominant open format for robot learning data, but decoding frames is expensive. Here's how we made Daft's native LeRobot reader up to 15× faster.
Finding a Needle in the Haystack: Querying Physical AI Data with Daft
Pose + semantic search over Apple's EgoDex hand-manipulation dataset with Daft: SigLIP embeddings meet hand-pose geometry. Ctrl+F for physical AI data.
Disk is the data plane: Flight Shuffle in Daft
How Daft rebuilt distributed shuffle around Arrow Flight, local disk, and streaming reads to handle multi-terabyte workloads.
First-class observability in Daft
A new dashboard, per-operator memory attribution, and OTel endpoints for your existing collector. Everything you need to see what Daft is doing with your query.
Scaling As-of Joins
How we built, broke, and re-built our ASOF joins — 5.5x faster, half the memory of pandas, and scaled to a distributed cluster.
daft.VideoFile: Seek Lazily, Get Frames
daft.VideoFile decodes only the frames you need. Keyframes, time-sampled, or windowed seek, built for robotics datasets, dashcams, and moderation queues.
Daft Extensions Featuring daft-h3: Native Rust Performance, Community Owned
Daft now supports native extensions via Apache Arrow's C Data Interface. daft-h3 is the first community extension — 9 Rust-native H3 geospatial functions, 3–16x faster than Python UDFs.
Audio transcription at scale with daft.AudioFile
How to transcribe thousands of audio files with Whisper using daft.AudioFile — handling resampling, silence splitting, and worker-resident model loading without the boilerplate.
Image Embeddings: Tutorial & Examples
Learn about the concept of image embeddings, their various use cases, and best practices for handling them in data processing workflows.
daft.File: Lazy Metadata Filters
Filter millions of files by path, size, and content type before opening any of them. Cheap operations first, expensive operations on the survivors.
Multimodal Embeddings: Tutorial & Examples
Learn multimodal embedding techniques for cross-modal search, recommendation systems, and content moderation applications.
Daft UDF Patterns: Four Patterns, One Notebook
Row-wise, generator, async, and stateful UDFs — one notebook, one dataset, runnable side by side.
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.
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.
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.
Tuning Daft's Distributed UDFs: Lessons from ByteDance
Learn from the ByteDance Volcengine LAS Team on how to optimize Daft UDFs on Ray. Discover the formula to evenly distribute data across actors.
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.
Introducing Dynamic Batching: Auto-Tuning for Daft Pipelines
Manually tuning batch sizes is hard. So I implemented dynamic batching to never deal with it ever again.
How We Use AI Coding Agents
Our engineering team's best practices for working with AI coding agents.
How Sourcetable Built the World's First AI Spreadsheet with Daft
Sourcetable CTO Andy Grosser discusses their data infrastructure choices and why reliability and scale drove their architecture decisions.
Multimodal Structured Outputs: Evaluating VLM Image Understanding at Scale
Leveraging ablation for contrastive image understanding evaluation in Daft
Processing 99% of U.S. Caselaw for Under $1 in the Common Pile
How Teraflop AI processed 7 million court documents and 40 million pages spanning 365 years of U.S. caselaw for under a dollar using Daft.
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.
Cutting LLM Batch Inference Time in Half: Dynamic Prefix Bucketing at Scale
Learn how Dynamic Prefix Bucketing reduces LLM batch inference time, improves throughput, and unlocks faster multimodal processing at scale.
Simplifying Voice AI Analytics with Daft: Transcription, Summaries, and Embeddings at Scale
Build a Voice AI analytics pipeline with Daft and Faster-Whisper to convert raw audio into searchable transcripts, summaries, and embeddings at scale.
Using PyTorch DataLoaders to Streamline Multimodal Data
Learn how PyTorch's DataLoader streamlines deep learning pipelines by efficiently loading and shuffling data in batches.
Benchmarks for Multimodal AI: Spark, Ray Data, and Daft
Multimodal AI workloads break traditional data engines. Daft ran 2-7x faster than Ray Data and 4-18x faster than Spark while finishing jobs reliably across audio, video, document, and image workloads.
Introducing Flotilla: Simplifying Multimodal Data Processing at Scale
Flotilla, Daft's new distributed engine, processes terabytes of multimodal data in a single query up to 18x faster than Spark and Ray Data, while running efficiently, reliably, and without manual tuning.
Exploring Daft's Local Execution: The Swordfish Engine
Explore how Daft's Rust-powered engine executes DataFrame and SQL queries. Learn how Swordfish enables fast, streaming image processing at scale.
After the First Run
Using Daft's observability tools to uncover performance pitfalls
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