In this section, we will set up an ETL (Extract, Transform, Load) pipeline using PostgreSQL and Python. The goal is to efficiently load raw data, perform necessary data cleaning and transformations, and store the processed data in a structured format for analysis.
active 2025-03-02 → 2025-03-08 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 7 days from 2025-03-02 to 2025-03-08. Pushes: 6 total, peak 3 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
- Pushes
- Pull requests
- Issues
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Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| memerazo | 6 | 6 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.