Welcome to the Eventual blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Daft v0.7.15: Safe Type Conversions, Flight Shuffle Optimizations, and PostgreSQL Support
Daft v0.7.15 ships with try_cast for safe type conversion, Flight shuffle LZ4 compression, UUIDv7 timestamp extraction, and PostgreSQL support.
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
Daft v0.7.4 completes its arrow-rs migration, adds Apache OpenDAL storage support, Flight shuffle for Flotilla, and a full observability stack.
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.
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.
Daft OSS: New Governance Model
Today, we're introducing updates to the Daft OSS governance model defining new roles for contributors and maintainers with expanded permissions.
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.
Why I joined Eventual
Chris Kelloggs shares why he joined Eventual to build open-source, distributed systems for large-scale AI and multimodal data workloads
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.