Optimize Python & Upload to BigQuery
Budget: $10 – $30 CAD
I maintain a Python script that prepares data and pushes it into Google BigQuery, but the code has grown messy. I want it refactored so that any developer can open the file and understand what’s happening at a glance, then watch it load cleanly into my BigQuery table.
Here’s what I need from you:
• Put readability first. Rename variables so they instantly convey intent, weave concise yet helpful inline and block comments throughout, and reorganize the code into neat, logical sections or functions.
• Stay faithful to PEP 8 standards and keep an eye out for anything that could trip up future performance, but speed tuning isn’t the main goal—clarity is.
• Verify the end-to-end pipeline: authenticate with my service-account JSON, run the script, and confirm rows land in the correct BigQuery dataset with the expected schema.
Deliverables
1. Refactored .py file(s) with improved comments, naming, and structure.
2. A short change log summarising key refactor decisions.
3. Evidence of a successful BigQuery load (console log or screenshot).
The project is small and focused; if you are comfortable with Python 3.x, pandas, and the BigQuery Python client, this should be a smooth engagement.
Here’s what I need from you:
• Put readability first. Rename variables so they instantly convey intent, weave concise yet helpful inline and block comments throughout, and reorganize the code into neat, logical sections or functions.
• Stay faithful to PEP 8 standards and keep an eye out for anything that could trip up future performance, but speed tuning isn’t the main goal—clarity is.
• Verify the end-to-end pipeline: authenticate with my service-account JSON, run the script, and confirm rows land in the correct BigQuery dataset with the expected schema.
Deliverables
1. Refactored .py file(s) with improved comments, naming, and structure.
2. A short change log summarising key refactor decisions.
3. Evidence of a successful BigQuery load (console log or screenshot).
The project is small and focused; if you are comfortable with Python 3.x, pandas, and the BigQuery Python client, this should be a smooth engagement.