Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.

Daft v0.7.15 ships with try_cast for safe type conversion, Flight shuffle LZ4 compression, UUIDv7 timestamp extraction, and PostgreSQL support.

Physical AI has become a real trend, but is there something real here or is it just hype?

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.

30 contributors shipped Daft v0.7.10 — the most participation in any Daft release to date. The result: 41 new features and functions across distributed joins, duplicate detection, temporal arithmetic,

How to transcribe thousands of audio files with Whisper using daft.AudioFile — handling resampling, silence splitting, and worker-resident model loading without the boilerplate.

Learn about the concept of image embeddings, their various use cases, and best practices for handling them in data processing workflows.

Filter millions of files by path, size, and content type before opening any of them. Cheap operations first, expensive operations on the survivors.

Learn multimodal embedding techniques for cross-modal search, recommendation systems, and content moderation applications.

Migrating ETL workloads from Spark means hitting gaps in date arithmetic — functions like `date_add`, `date_diff`, and epoch conversions that Spark users take for granted. Daft v0.7.9 closes that gap

Daft's query dashboard now shows you exactly where time is going. Slow operators light up red, completed nodes turn green, and arrows trace the data flow through your pipeline. No more guessing which