uProc saves time and engineering resources by using n8n to scrape banking data from a multi-page website
Collecting banking reference data (Swift codes) from a multi-page website was challenging because the data was scattered across sources in different formats and sometimes outdated. The prior Python/Scrapy approach required repetitive manual coding work — selecting HTML tags, formatting, and processing — making it time-consuming.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Cache directory initialization
The Execute Command node automatically creates a local cache directory before the web-scraping process starts to avoid scraping the same pages twice.
Tools used
n8nMongoDBuProcScrapy
Outcome
Miquel replaced the Python scripts with a 22-node low-code n8n workflow that scrapes all country pages on theswiftcodes.com and stores the data in MongoDB, saving time and engineering resources by automating away repetitive coding.
What failed first
Python scripts using Scrapy were technically adequate but required extensive repetitive manual coding — selecting tags, formatting, and processing data — making the approach impractical to maintain.