A high-performance data engine providing simple and reliable data processing for any modality and scale.
Any Modality. Radical potential.
A high-performance data engine providing simple and reliable data processing for any modality and scale.
Unified multimodal data processing
Break down data silos with a single framework that handles structured tables, unstructured text, and rich media like images—all with the same intuitive API. Why juggle multiple tools when one can do it all?
Python-native, no JVM required
Built for modern AI/ML workflows with Python at its core and Rust under the hood. Skip the JVM complexity, version conflicts, and memory tuning to achieve 20x faster start times—get the performance without the Java tax.
Seamless scaling, from laptop to cluster
Start local, scale global—without changing a line of code. Daft's Rust-powered engine delivers blazing performance on a single machine and effortlessly extends to distributed clusters when you need more horsepower.
Unified multimodal data processing
Break down data silos with a single framework that handles structured tables, unstructured text, and rich media like images—all with the same intuitive API. Why juggle multiple tools when one can do it all?
Python-native, no JVM required
Built for modern AI/ML workflows with Python at its core and Rust under the hood. Skip the JVM complexity, version conflicts, and memory tuning to achieve 20x faster start times—get the performance without the Java tax.
Seamless scaling, from laptop to cluster
Start local, scale global—without changing a line of code. Daft's Rust-powered engine delivers blazing performance on a single machine and effortlessly extends to distributed clusters when you need more horsepower.
Use Cases
Large Scale Document Processing
- This example demonstrates Daft's ability to seamlessly integrate with AI models to create text embeddings at scale.
- Using a pre-trained transformer model, Daft processes large document collections stored in cloud storage, converting text into embeddings using easy-to-use user-defined functions for downstream AI applications like semantic search or clustering.
[1]
Native Multimodal Processing
Process any data type—from structured tables to unstructured text and rich media—with native support for images, embeddings, and tensors in a single, unified framework.
[2]
Rust-Powered Performance
Experience breakthrough speed with our Rust foundation delivering vectorized execution and non-blocking I/O that processes the same queries with 5x less memory while consistently outperforming industry standards by an order of magnitude.
[3]
Seamless ML Ecosystem Integration
Slot directly into your existing ML workflows with zero friction—whether you're using PyTorch, NumPy, Pandas, or HuggingFace models, Daft works where you work.
[4]
Universal Data Connectivity
Access data anywhere it lives—cloud storage (S3, Azure, GCS), modern table formats (Iceberg, Delta Lake, Hudi), or enterprise catalogs (Unity, AWS Glue)—all with zero configuration.
[5]
Push Your Code to Your Data
Bring your Python functions directly to your data with zero-copy UDFs powered by Apache Arrow, eliminating data movement overhead and accelerating processing speeds.
[6]
Out of the Box Reliability
Deploy with confidence—intelligent memory management prevents OOM errors while sensible defaults eliminate configuration headaches, letting you focus on results, not infrastructure.
Tony Wang
Data @ Anthropic, PhD @ Stanford
Patrick Ames
Principal Engineer @ Amazon
Maurice Weber
PhD AI Researcher @ Together AI
Alexander Filipchik
Head Of Infrastructure at City Storage Systems (CloudKitchens)
Apache Hudi
Apache Iceberg™
Amazon Web Services
Azure
Delta Lake
Google Cloud
Unity Catalog
PyTorch
Pandas
Ray
