Suggestions appear as you type. Use the up and down arrows to choose one and Enter to open it.

This page's audience our own tag's counts — open to see them
–Visits
–Page views
–Clicks to vendors
–Time on page
–Reading now
Clicks to vendors, by tool
  • –
Top countries
  • –
Devices
  • –

– · visits, page views and clicks counted by our own tag, which counts more than our checked analytics; every figure rounded down · how we count

Daft review

Free#20 of 36 in Data Transformation Tools

A code-first engine for distributed structured and multimodal data transformation.

7.3/10Editor score
Visit Daft
Daft7.3 Visit Daft

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Daft is an open-source data engine for teams transforming structured and multimodal data. Its Python DataFrame API supports filtering, transforming, aggregating, and writing data, while its SQL interface provides another way to query and transform datasets. The product handles images, audio, video, text, documents, and embeddings alongside formats such as CSV, JSON, Parquet, Apache Iceberg, Delta Lake, and Apache Hudi. It is aimed at data engineering, machine learning, analytics, and AI data preparation work, particularly where distributed execution or GPU-backed inference matters.

The strongest fit is a technical team that needs one transformation layer across local machines, Ray, and Kubernetes. User-defined Python functions support batching and distributed execution, and checkpointing enables resumable processing and idempotent Iceberg or Delta Lake writes. GPU-backed model inference and embedding workflows extend Daft beyond conventional tabular processing. Its ecosystem includes Amazon S3, Azure Blob Storage, Google Cloud Storage, Unity Catalog, Kafka, PostgreSQL, ClickHouse, Pandas, PyTorch, Hugging Face Datasets, and other storage and table systems, giving it a broad integration surface for engineering and AI pipelines.

Daft is free as open-source software, with a self-hosted deployment model and platforms including macOS, Linux, and an API. That makes it suitable for teams comfortable owning code and infrastructure, but less appropriate for buyers seeking a low-code transformation tool. Its broad multimodal and distributed scope can also be more than a small team needs for straightforward tabular jobs. Choose Daft when Python or SQL, multimodal processing, and distributed execution are central requirements; choose an alternative when visual workflows or a narrower, simpler transformation experience is the priority.

Daft pros and cons

  • Where it wins
    • Processes images, audio, video, text, documents, and embeddings
    • Supports Python, SQL, local, Ray, and Kubernetes execution
    • Connects storage, table formats, catalogs, databases, and files
  • Where it doesn't
    • Code-first workflows are less suited to low-code users
    • Self-hosted deployment requires technical infrastructure management
    • Its scope may exceed teams focused only on simple tabular transformations

Daft fact sheet, pricing and score →

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Last updated · How we research and update