Google Maps data extraction
Research firm, name withheldData
- A scraping architecture built to respect the source's access rules
- A scheduled collector with a queue, retries and monitoring
- Structured export to CSV and JSON, refreshed hourly
- Data accuracy
- 99.8%
- Provisional
- Records processed per hour
- 10,000+
- Provisional
- Manual steps after handover
- 0
- Provisional
Provisional figures are being re-checked with the client.
Manual data collection from Google Maps and Search was slow and error-prone, and the research team needed structured, current datasets rather than a one-off export.
The situation
A research team was collecting business listings and reviews from Google Maps and Search by hand. The work was slow, the data was inconsistent, and it was out of date by the time it was used.
What we built
A collector on Playwright and Python with a job queue, retries and monitoring, running on a schedule. Results are deduplicated and normalised into a fixed schema and exported hourly as CSV and JSON for the team's own tools.
Where we drew the line
The collector respects rate limits and the source's access rules, and the legal basis for the dataset was written down before we built it.
Next case
Automated presentation pipeline
Corporate agency, name withheldBusiness systems
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