Comparison
data-juicer vs unstructured
Verdict
Pick data-juicer if a Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation; pick unstructured if unstructured is an open-source ETL solution designed specifically to convert complex documents into structured data suitable for language models.
Markdown twin · data-juicer alternatives · unstructured alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | data-juicer | unstructured |
|---|---|---|
| Maintenance | Very active (4d since push) As of 4d · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- data-juicer
- Data processing for and with foundation models
- unstructured
- Convert documents to structured data effortlessly
Stars
- data-juicer
- 6.9k
- unstructured
- 15k
Forks
- data-juicer
- 404
- unstructured
- 1.3k
Open issues
- data-juicer
- 59
- unstructured
- 277
Language
- data-juicer
- Python
- unstructured
- HTML
Adopt for
- data-juicer
- A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation.
- unstructured
- Unstructured is an open-source ETL solution designed specifically to convert complex documents into structured data suitable for language models.
Persona
- data-juicer
- -
- unstructured
- -
Runtime
- data-juicer
- -
- unstructured
- -
License
- data-juicer
- Apache-2.0
- unstructured
- The tool is distributed under the Apache-2.0 license, which allows for free use, modification, and distribution as long as compatibility with the license terms is maintained.
Last pushed
- data-juicer
- Aug 13, 2026
- unstructured
- Jul 31, 2026
Categories
- data-juicer
- Data & Retrieval, Model Training
- unstructured
- Data & Retrieval, Model Training
Trust and health
Days since push
- data-juicer
- 4d
- unstructured
- 0d
Open issues (now)
- data-juicer
- 59
- unstructured
- 277
Stars delta
- data-juicer
- +166 (30d)
- unstructured
- Unknown
Open issues delta
- data-juicer
- -3 (30d)
- unstructured
- Unknown
Full report
- data-juicer
- Trust report
- unstructured
- Trust report
Typed relationship
data-juicer depends on unstructuredData-Juicer processes data which could include the output from unstructured documents processed by unstructured.
Shared compatibility
- Python · data-juicer: Python runtime · unstructured: Python runtime
Choose data-juicer if…
- data-juicer is primarily Python; unstructured is HTML.
- Data-Juicer processes data which could include the output from unstructured documents processed by unstructured.
- Tags unique to data-juicer: foundation-models, instruction-tuning, large language models, synthetic-data.
- When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning.
When NOT to use data-juicer
- If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation.
Choose unstructured if…
- unstructured is primarily HTML; data-juicer is Python.
- Requirements: Requires Docker; Building Docker images independently may require customizing the `Dockerfile` to include only necessary packages/requirements based on specific data parsing use; The base image `wolfi-base`, a regularly updated image, might cause build failures due to upstream changes but can be managed by specifying requirements..
- Data-Juicer processes data which could include the output from unstructured documents processed by unstructured.
- Tags unique to unstructured: data-pipelines, deep-learning, document-parser, document-processing.
- When you need to transform various types of unprocessed, complex documents (including PDFs, images with text) into formats that are easily usable by machine learning pipelines.
When NOT to use unstructured
- When your workflow is limited to only one type of data, as Unstructured might introduce unnecessary complexity due to its broad support for multiple data types.
- If you prefer a proprietary solution with dedicated enterprise-level support and features that are beyond the scope of an open-source project like Unstructured.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (datajuicer/data-juicer) · observed Aug 17, 2026
- GitHub forks (datajuicer/data-juicer) · observed Aug 17, 2026
- Last push (datajuicer/data-juicer) · observed Aug 13, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Unstructured-IO/unstructured) · observed Aug 1, 2026
- GitHub forks (Unstructured-IO/unstructured) · observed Aug 1, 2026
- Last push (Unstructured-IO/unstructured) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: data-juicer 6.9k · unstructured 15k (synced Aug 17, 2026).
Common questions
- What is the difference between data-juicer and unstructured?
- data-juicer: Data processing for and with foundation models. unstructured: Convert documents to structured data effortlessly. See the comparison table for live GitHub stats and shared categories.
- When should I choose data-juicer over unstructured?
- Choose data-juicer over unstructured when data-juicer is primarily Python; unstructured is HTML; Data-Juicer processes data which could include the output from unstructured documents processed by unstructured; Tags unique to data-juicer: foundation-models, instruction-tuning, large language models, synthetic-data; When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning.
- When should I choose unstructured over data-juicer?
- Choose unstructured over data-juicer when unstructured is primarily HTML; data-juicer is Python; Requirements: Requires Docker; Building Docker images independently may require customizing the
Dockerfileto include only necessary packages/requirements based on specific data parsing use; The base imagewolfi-base, a regularly updated image, might cause build failures due to upstream changes but can be managed by specifying requirements.; Data-Juicer processes data which could include the output from unstructured documents processed by unstructured; Tags unique to unstructured: data-pipelines, deep-learning, document-parser, document-processing; When you need to transform various types of unprocessed, complex documents (including PDFs, images with text) into formats that are easily usable by machine learning pipelines. - When should I avoid data-juicer?
- If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation.
- When should I avoid unstructured?
- When your workflow is limited to only one type of data, as Unstructured might introduce unnecessary complexity due to its broad support for multiple data types. If you prefer a proprietary solution with dedicated enterprise-level support and features that are beyond the scope of an open-source project like Unstructured.
- Is data-juicer or unstructured more popular on GitHub?
- unstructured has more GitHub stars (15,238 vs 6,897). Stars measure visibility, not whether either tool fits your constraints.
- Are data-juicer and unstructured open source?
- Yes - both are open-source projects on GitHub (data-juicer: Apache-2.0, unstructured: Apache-2.0).
- Where can I find alternatives to data-juicer or unstructured?
- GraphCanon lists graph-backed alternatives at data-juicer alternatives and unstructured alternatives (data-juicer markdown twin, unstructured markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, data-juicer or unstructured?
- data-juicer: Very active. unstructured: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for data-juicer and unstructured?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: data-juicer trust report; unstructured trust report.