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