Welcome to the Daft blog

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

Another killer release featuring arrow-rs migration, Apache OpenDAL support, Flight shuffle, better metrics, and Tencent Cloud COS integration.

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.

daft.File: Work with Any File, Anywhere

Distributed Random Access for Audio, Video, Documents, and Code

Tuning Daft's Distributed UDFs: Lessons from ByteDance

Distributed Model Inference with Daft

A deep dive into Daft’s distributed execution engine, Flotilla, for multimodal data pipelines

Introducing Dynamic Batching: Auto-Tuning for Daft Pipelines

I Got Tired of Tuning Batch Sizes, So I Made Them Tune Themselves

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.

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.

Multimodal Structured Outputs: Evaluating VLM Image Understanding at Scale

Leveraging ablation for contrastive image understanding evaluation in Daft

Cutting LLM Batch Inference Time in Half: Dynamic Prefix Bucketing at Scale

A new inference backend that maximizes batch inference throughput.