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.
From v0.2 to v0.3: Harder, Better, Faster, Stronger
Join us on the journey from Daft v0.2 to v0.3! Daft v0.3 was released last month, marking the first minor version increment in almost 10 months.
Introducing Daft-SQL for High-Performance Data Exploration
A SQL API enabling users to interact with their data in a new but familiar way. Learn how Daft-SQL brings fast, scalable querying to multimodal workloads, helping teams explore large datasets efficiently with a distributed engine.
Reading Delta Lake with Daft
Discover how Daft reads Delta Lake tables efficiently, giving teams fast access to large datasets and seamless integration into data workflows.
Adversarial file reading: from 10,000 small CSVs to massive Parquet files
Learn how adversarial file reading speeds up data ingestion at scale, enabling fast conversion from thousands of CSVs into efficient Parquet files.
Announcing Daft 0.2: 10x faster IO from S3
Daft 0.2 introduces a redesigned IO layer with 10x faster S3 reads and noticeable speedups across data loading, compute, and end-to-end workflows.
Working with the Apache Parquet file format
This guide shows how Apache Parquet boosts read performance, lowers storage use, and supports efficient workflows for large analytical datasets.
Introducing Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Discover how Daft accelerates multimodal data processing with a high-performance distributed dataframe engine built for modern AI and analytics tasks.