What is Daft?
Daft is a powerful and versatile data engine designed to simplify and accelerate data engineering, analytics, and machine learning/AI workflows. Built in Rust and featuring both SQL and Python DataFrame interfaces, Daft offers a seamless experience from local development to massive distributed workloads. Experience the speed of DuckDB, the user-friendliness of Polars, and the scalability of Apache Spark – all within a single unified platform.
Key Features:
Unified Interface:🐍 Access data using familiar SQL or Python DataFrame APIs, enabling diverse data operations within one system.
Scalable Performance:⚡️ Effortlessly transition from local prototyping to large-scale distributed processing for petabyte-scale datasets.
Blazing Fast:🚀 Built on Rust for exceptional speed and efficiency, outperforming traditional frameworks like Spark.
AI/ML Integration:🤖 Seamlessly integrate with popular Python libraries like PyTorch and Ray for streamlined machine learning workflows.
Cloud Native:☁️ Native support for cloud storage like Amazon S3, enabling efficient data loading and processing.
Use Cases:
ETL Pipelines:A data engineer can use Daft to efficiently extract data from various sources, transform it using SQL or Python, and load it into a data warehouse like Delta Lake. Daft's scalability allows them to process massive datasets with ease.
Data Exploration and Analytics:A data analyst can leverage Daft's interactive SQL and Python interfaces to quickly explore and analyze data locally, then seamlessly scale their analysis to a distributed cluster for deeper insights on larger datasets.
Machine Learning Model Training:A machine learning engineer can use Daft to efficiently load and preprocess large datasets for model training. Direct integration with PyTorch and Ray simplifies data feeding into models and accelerates training on GPUs.
Conclusion:
Daft empowers data professionals across various domains with its unified, scalable, and performant data engine. By combining the strengths of popular data tools, Daft simplifies complex workflows and accelerates data-driven insights. Whether you're building data pipelines, running analytics, or training machine learning models, Daft offers a compelling solution for all your data needs.
FAQs:
How does Daft compare to Apache Spark?While both are distributed data processing frameworks, Daft is built in Rust for superior speed and efficiency. Daft also offers a more user-friendly Python experience without the complexities of the JVM.
Can I use Daft with my existing cloud storage?Yes, Daft natively supports cloud storage services like Amazon S3, allowing you to seamlessly access and process data stored in the cloud.
What programming languages does Daft support?Daft primarily supports SQL and Python for data manipulation and analysis. Its Python DataFrame API is particularly well-suited for users familiar with libraries like Pandas and Polars.
More information on Daft
Launched
2022-04
Pricing Model
Starting Price
Global Rank
3186578
Follow
Month Visit
5.7K
Tech used
Google Analytics,Google Tag Manager,cdnjs,Cloudflare CDN,Read the Docs,Sphinx,Font Awesome,Google Fonts,Bootstrap,Highlight.js,jQuery,Pygments,Underscore.js,Gzip,HTTP/3
Top 5 Countries
46.27%
19.06%
16.08%
10.36%
6.44%
United States
India
Italy
Poland
Netherlands
Traffic Sources
7.62%
1.03%
0.09%
9.37%
43.21%
38.5%
social
paidReferrals
mail
referrals
search
direct
Source: Similarweb (Sep 25, 2025)
Daft was manually vetted by our editorial team and was first featured on 2024-12-06.
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