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license: osl-3.0
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---
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license: osl-3.0
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---
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# Process to Generate DuckDB Dataset
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## 1. Load Repository Metadata
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- Read `repo_metadata.json` from [GitHub Public Repository Metadata](https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars/data)
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- Normalize JSON into three lists:
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- **Repositories** → general metadata (stars, forks, license, etc.).
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- **Languages** → repo-language mappings with size.
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- **Topics** → repo-topic mappings.
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- Convert lists into Pandas DataFrames: `df_repos`, `df_languages`, `df_topics`.
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## 2. Enhance with BigQuery Data
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- Create a temporary BigQuery table (`repo_list`) containing repository names.
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- Query **GitHub Archive (`githubarchive.month.2024*`)**:
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- Collect contributors from `PullRequestEvent`.
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- Collect stargazers from `WatchEvent`.
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- Merge BigQuery results back into `df_repos`.
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## 3. Preprocess for DuckDB
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- Aggregate topics by repository:
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- Join multiple topics into a single pipe-separated string.
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- Create `df_topics_agg`.
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## 4. Write to DuckDB
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- Connect to DuckDB using **SQLAlchemy**.
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- Write DataFrames into DuckDB:
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- `repos` → enriched repository metadata.
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- `repo_languages` → repo-language relations.
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- `repo_topics` → aggregated topics.
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- Add index on `repo_topics.topics` for faster querying:
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```sql
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CREATE INDEX topic_idx ON repo_topics (topics);
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