--- title: "data-prep-kit vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/data-prep-kit-data-prep-kit-vs-sindresorhus-awesome" tools: ["data-prep-kit-data-prep-kit", "sindresorhus-awesome"] --- # data-prep-kit vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick data-prep-kit when license: data-prep-kit is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, data-prep-kit is Apache-2.0. [data-prep-kit](https://data-prep-kit.github.io/data-prep-kit/) reports 947 GitHub stars, 251 forks, and 223 open issues, last pushed Jun 22, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [data-prep-kit's repository](https://github.com/data-prep-kit/data-prep-kit) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [data-prep-kit](/tools/data-prep-kit-data-prep-kit.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Open source project for data preparation for GenAI applications | 😎 Curated list of awesome topics including hardware resources | | Stars | 947 | 484,026 | | Forks | 251 | 35,799 | | Open issues | 223 | 92 | | Language | HTML | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Data & Retrieval, LLM Frameworks, Developer Tools | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [data-prep-kit](/tools/data-prep-kit-data-prep-kit.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Days since push | 18d | 11d | | Open issues (now) | 223 | 92 | | Owner type | Organization | User | | Full report | [trust report](/tools/data-prep-kit-data-prep-kit/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose data-prep-kit if… - License: data-prep-kit is Apache-2.0, awesome is CC0-1.0. - Tags unique to data-prep-kit: data-prep, data-preprocessing-pipelines, datarecipes, code-quality. - Also covers Data & Retrieval, Developer Tools. ### Choose awesome if… - License: awesome is CC0-1.0, data-prep-kit is Apache-2.0. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 947) - visibility, not fit. ## When NOT to use data-prep-kit - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between data-prep-kit and awesome? data-prep-kit: Open source project for data preparation for GenAI applications. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose data-prep-kit over awesome? Choose data-prep-kit over awesome when License: data-prep-kit is Apache-2.0, awesome is CC0-1.0; Tags unique to data-prep-kit: data-prep, data-preprocessing-pipelines, datarecipes, code-quality; Also covers Data & Retrieval, Developer Tools. ### When should I choose awesome over data-prep-kit? Choose awesome over data-prep-kit when License: awesome is CC0-1.0, data-prep-kit is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 947) - visibility, not fit. ### When should I avoid data-prep-kit? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is data-prep-kit or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 947). Stars measure visibility, not whether either tool fits your constraints. ### Are data-prep-kit and awesome open source? Yes - both are open-source projects on GitHub (data-prep-kit: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to data-prep-kit or awesome? GraphCanon lists graph-backed alternatives at [data-prep-kit alternatives](/tools/data-prep-kit-data-prep-kit/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([data-prep-kit markdown twin](/tools/data-prep-kit-data-prep-kit/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/alternatives.md)), 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](/compare/data-prep-kit-data-prep-kit-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, data-prep-kit or awesome? data-prep-kit: Active. awesome: 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-prep-kit and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [data-prep-kit trust report](/tools/data-prep-kit-data-prep-kit/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=data-prep-kit-data-prep-kit`](/api/graphcanon/graph?tool=data-prep-kit-data-prep-kit) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_