daft
Distributed Dataframes for Multimodal Data
Decision gist · record as of 2026-08-14
Yes, if you need to process multimodal data (images, audio, video, text) at scale with built-in AI operations. The active maintenance, recent releases, and lack of known vulnerabilities are positive signals. Verify the Apache 2.0 license terms match your use case, and confirm that medium install friction (compiled Rust wheels) is acceptable for your deployment environment. Not necessary if you work only with structured data or single-modality inputs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or higher; compiled wheels available for macOS (x86_64, arm64), Linux (x86_64, aarch64), and Windows (x86_64).
- Medium install friction due to compiled Rust components (wheels provided for common platforms).
- Active maintenance with a recent release (9 days ago) and 5709 repository stars.
License · maintenance · safety
(unclear) — License treatment is unclear in the fact sheet; the description states Apache 2.0, but SPDX and raw license fields are not populated. Verify the actual license terms before adopting in proprietary projects.
last release 2026-08-05 (9 days) · last repo commit 2026-08-12 · 5,709 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,118,600 downloads/mo, #4,345 on PyPI
Alternatives
Verify before relying
pip install daft
import daft
df = daft.from_pydict({"col": [1, 2, 3]})
df.show()- Whether Apache 2.0 license is correctly applied given unclear license_treatment in metadata
- Performance benchmarks and scaling characteristics on distributed clusters
- Maturity and stability guarantees for production AI workloads
What it is and what it does
Daft is a Python-native, Rust-powered distributed dataframe engine designed for AI and multimodal workloads. It processes images, audio, video, and structured data in a single framework, with built-in operations for running LLM prompts, generating embeddings, and classifying data at scale. The engine supports starting locally and scaling to distributed clusters via Ray or Kubernetes, and can access data from S3, GCS, Iceberg, Delta Lake, Hugging Face, and Unity Catalog.
The package depends on pyarrow, fsspec, tqdm, typing-extensions, and packaging. It targets developers working with mixed data types (text, images, audio, video) who need to process them together at scale without JVM overhead. The framework handles memory management and provides sensible defaults to reduce configuration burden.
Use it for
- Process e-commerce product images alongside structured metadata and run AI inference on them at scale
- Load multimodal datasets from Hugging Face and apply LLM prompts or embeddings to both text and images
- Scale local data processing workflows to distributed Ray clusters without rewriting code
- Combine audio, video, and structured data in a single pipeline for media analysis tasks
- Access and transform data from data lakes (Iceberg, Delta Lake) with native multimodal support
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to process multimodal data (images, audio, video, text) at scale with built-in AI operations.
The active maintenance, recent releases, and lack of known vulnerabilities are positive signals. Verify the Apache 2.0 license terms match your use case, and confirm that medium install friction (compiled Rust wheels) is acceptable for your deployment environment. Not necessary if you work only with structured data or single-modality inputs.
Install
daft on PyPI
Before you install
Medium install friction due to compiled Rust components (wheels provided for common platforms). Active maintenance with a recent release (9 days ago) and 5709 repository stars. Requires Python 3.10 or higher.
Requires Python 3.10 or higher; compiled wheels available for macOS (x86_64, arm64), Linux (x86_64, aarch64), and Windows (x86_64).
License in practice
License treatment is unclear in the fact sheet; the description states Apache 2.0, but SPDX and raw license fields are not populated. Verify the actual license terms before adopting in proprietary projects.
Quickstart
pip install daft
import daft
df = daft.from_pydict({"col": [1, 2, 3]})
df.show()Verify before relying
- Whether Apache 2.0 license is correctly applied given unclear license_treatment in metadata
- Performance benchmarks and scaling characteristics on distributed clusters
- Maturity and stability guarantees for production AI workloads
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagespyarrowfsspectqdmtyping-extensionspackaging |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,118,600 / month, #4,345 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: daft-0.7.23-cp310-abi3-macosx_10_12_x86_64.whl; daft-0.7.23-cp310-abi3-macosx_11_0_arm64.whl; daft-0.7.23-cp310-abi3-manylinux_2_24_aarch64.whl; daft-0.7.23-cp310-abi3-manylinux_2_24_x86_64.whl; daft-0.7.23-cp310-abi3-win_amd64.whl
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