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

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.

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

Native Extensions via Stable C ABI, Live Query Dashboard, and 2-5x faster Parquet Reads on Nested Types

Daft Observability Roadmap: metrics, OTEL integration, real-time dashboards, and DataFrame APIs for debugging and monitoring distributed pipelines.

Daft v0.7.4 completes its arrow-rs migration, adds Apache OpenDAL storage support, Flight shuffle for Flotilla, and a full observability stack.