Daft
Daft: A code-first engine for distributed structured and multimodal data transformation. Ranked #20 of 36 in Data Transformation Tools by our editors (7.3/10); pricing: Free plan; best for distributed multimodal data transformation.
At a glance
- Editor score7.3 / 10
- PricingFree plan
- Best forDistributed multimodal data transformation
- Free planYes
- Paid fromFree
- Deployment modelSelf_hosted
- Facts checked22 Sep 2026
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
Our verdict on Daft
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.
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Daft pricing
Daft fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Deployment model | Self_hosted |
| Transformation interface | Code |
| Supported data formats | CSV, JSON, line-delimited JSON, Parquet, Apache Hudi, Apache Iceberg, Delta Lake, Apache Paimon, Lance, text, images, audio, video, PDF documents, JSON and nested data |
| Version control | Not verified |
| Workflow orchestration | Not verified |
| Data quality checks | Not verified |
| Deployment | Self-hosted |
| Platforms | macOS, Linux |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 14 integrations: Apache Hudi, Apache Iceberg, Amazon S3, Azure Blob Storage, Google Cloud Storage, Delta Lake … |
| Pricing | Free plan |
| Website | daft.ai |
| Facts checked | 22 Sep 2026 |
Daft integrations
Daft lists 14 integrations on its own site.
- Apache Hudi
- Apache Iceberg
- Amazon S3
- Azure Blob Storage
- Google Cloud Storage
- Delta Lake
- Unity Catalog
- Ray
- Pandas
- PyTorch
- Apache Kafka
- PostgreSQL
- ClickHouse
- Hugging Face Datasets
Alternatives to Daft
- DataformA free, managed workflow service for SQL transformation and testing in BigQuery.9.2
- Alteryx DesignerA broad visual toolkit for enterprise analysts working across complex data environments.9.1
- Apache BeamA broad, portable framework for engineering teams building code-driven data pipelines.9.0
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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