Daft dataframes can load any data such as PDF documents, images, protobufs, csv, parquet and audio files into a table dataframe structure for easy querying

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Website • Docs • Installation • Daft Quickstart • Community and Support

Daft: Unified Engine for Data Analytics, Engineering & ML/AI

Daft is a distributed query engine for large-scale data processing using Python or SQL, implemented in Rust.

  • Familiar interactive API: Lazy Python Dataframe for rapid and interactive iteration, or SQL for analytical queries

  • Focus on the what: Powerful Query Optimizer that rewrites queries to be as efficient as possible

  • Data Catalog integrations: Full integration with data catalogs such as Apache Iceberg

  • Rich multimodal type-system: Supports multimodal types such as Images, URLs, Tensors and more

  • Seamless Interchange: Built on the Apache Arrow In-Memory Format

  • Built for the cloud: Record-setting I/O performance for integrations with S3 cloud storage

Table of Contents

About Daft

Daft was designed with the following principles in mind:

  1. Any Data: Beyond the usual strings/numbers/dates, Daft columns can also hold complex or nested multimodal data such as Images, Embeddings and Python objects efficiently with it’s Arrow based memory representation. Ingestion and basic transformations of multimodal data is extremely easy and performant in Daft.

  2. Interactive Computing: Daft is built for the interactive developer experience through notebooks or REPLs - intelligent caching/query optimizations accelerates your experimentation and data exploration.

  3. Distributed Computing: Some workloads can quickly outgrow your local laptop’s computational resources - Daft integrates natively with Ray for running dataframes on large clusters of machines with thousands of CPUs/GPUs.

Getting Started

Installation

Install Daft with pip install daft.

For more advanced installations (e.g. installing from source or with extra dependencies such as Ray and AWS utilities), please see our Installation Guide

Quickstart

Check out our quickstart!

In this example, we load images from an AWS S3 bucket’s URLs and resize each image in the dataframe:

import daft

# Load a dataframe from filepaths in an S3 bucket
df = daft.from_glob_path("s3://daft-public-data/laion-sample-images/*")

# 1. Download column of image URLs as a column of bytes
# 2. Decode the column of bytes into a column of images
df = df.with_column("image", df["path"].url.download().image.decode())

# Resize each image into 32x32
df = df.with_column("resized", df["image"].image.resize(32, 32))

df.show(3)

Dataframe code to load a folder of images from AWS S3 and create thumbnails

Benchmarks

Benchmarks for SF100 TPCH

To see the full benchmarks, detailed setup, and logs, check out our benchmarking page.

More Resources

  • Daft Quickstart - learn more about Daft’s full range of capabilities including dataloading from URLs, joins, user-defined functions (UDF), groupby, aggregations and more.

  • User Guide - take a deep-dive into each topic within Daft

  • API Reference - API reference for public classes/functions of Daft

Contributing

To start contributing to Daft, please read CONTRIBUTING.md

Here’s a list of good first issues to get yourself warmed up with Daft. Comment in the issue to pick it up, and feel free to ask any questions!

Telemetry

To help improve Daft, we collect non-identifiable data via our own analytics as well as Scarf (https://scarf.sh).

To disable this behavior, set the following environment variables: - DAFT_ANALYTICS_ENABLED=0 - SCARF_NO_ANALYTICS=true or DO_NOT_TRACK=true

The data that we collect is:

  1. Non-identifiable: Events are keyed by a session ID which is generated on import of Daft

  2. Metadata-only: We do not collect any of our users’ proprietary code or data

  3. For development only: We do not buy or sell any user data

Please see our documentation for more details.

https://static.scarf.sh/a.png?x-pxid=31f8d5ba-7e09-4d75-8895-5252bbf06cf6

Related Projects

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Query Optimizer

Multimodal

Distributed

Arrow Backed

Vectorized Execution Engine

Out-of-core

Daft

Yes

Yes

Yes

Yes

Yes

Yes

Pandas

No

Python object

No

optional >= 2.0

Some(Numpy)

No

Polars

Yes

Python object

No

Yes

Yes

Yes

Modin

Eagar

Python object

Yes

No

Some(Pandas)

Yes

Pyspark

Yes

No

Yes

Pandas UDF/IO

Pandas UDF

Yes

Dask DF

No

Python object

Yes

No

Some(Pandas)

Yes

Check out our dataframe comparison page for more details!

License

Daft has an Apache 2.0 license - please see the LICENSE file.

Project links

Data verified by PyPI on May 31, 2025
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.7.24)

2 maintainers

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Credits

Author: Eventual Inc

Maintainer: Sammy Sidhu

GitHub Statistics

Data verified by PyPI on May 31, 2025
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.9

Provides Extra

all aws azure deltalake gcp hudi iceberg lance numpy pandas ray spark sql unity viz

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