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I believe that's mostly the problem DoltHub (and source) is trying to solve, albeit that everyone is responsible for their own means of acquiring the data
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scraper
Nodejs web scraper. Contains a command line, docker container, terraform module and ansible roles for distributed cloud scraping. Supported databases: SQLite, MySQL, PostgreSQL. Supported headless clients: Puppeteer, Playwright, Cheerio, JSdom. (by get-set-fetch)
Store the scrape progress (to-be-scraped / in-progress / scraped / in-error URLs) in a database shared by all participants and scrape in parallel with as many machines as the db load permits. Got a connection timeout / IP is blocked on one machine ? Update the scrape status for the corresponding URL and let another machine retry it. https://github.com/get-set-fetch/scraper (written in typescript) follows this idea. Using Terraform from a simple config file you can adjust the number of scraper instances to be deployed in cloud at startup and during the scraping process. In benchmarks a PostgreSQL server running on a DigitalOcean vm with 4vCPU, 8GB memory allows for ~2000 URLs to be scraped per second (synthetic data, no external traffic). From my own experience this is almost never the bottleneck. Obeying robots.txt crawl-delay will surely put you under this limit. Disclaimer: I'm the npm package author.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
