Eventual-Inc/Daft

What it solves

Daft addresses the challenge of processing massive amounts of multimodal data—such as images, audio, video, and embeddings—alongside traditional structured data. It eliminates the need for complex JVM-based setups and provides a high-performance way to run AI operations at scale.

How it works

It is a Python-native data engine powered by Rust for high performance. Daft uses a vectorized execution engine and an Arrow-backed architecture to handle data efficiently. It can run locally or scale to distributed clusters using Ray or Kubernetes, and it connects to various data sources including S3, GCS, Iceberg, Delta Lake, and Hugging Face.

Who it’s for

Data engineers and AI practitioners who need to process large-scale multimodal datasets and integrate AI inference (like LLM prompts or embedding generation) directly into their data pipelines.

Highlights

• Native Multimodal Support: Handles images, audio, and video in a single framework.
• Built-in AI Ops: Direct integration for running LLM prompts, generating embeddings, and classifying data via OpenAI, Transformers, or custom models.
• Rust-Powered: Combines Python's ease of use with Rust's speed.
• Flexible Scaling: Seamlessly moves from local execution to distributed clusters.
• Universal Connectivity: Native access to cloud storage and modern table formats like Iceberg and Delta Lake.