Welcome to the Eventual blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Turning robot video into training-ready data with daft-physical-ai
A Python library built on Daft for turning robot video into training-ready data, starting with hand tracking and reward scoring as UDFs, with more to come.
Eventual Raises $30M to Build the Future of Data Processing
We've raised $30M to build generational technology for simple, reliable, and performant data processing across all modalities and regardless of scale.
We cloned over 15,000 repos to find the best developers
An adventure in AI and data engineering to analyze developers across Github
High-Performance File System Support With DeepSeek 3FS
Learn how Daft integrates with DeepSeek SmallPond 3FS to deliver faster file access and efficient data handling for modern workloads.
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.