If you want to scrape a website into Airtable, the goal is usually the same: turn unstructured web pages into structured, searchable records.
Airtable doesn’t crawl websites itself. Instead, you use a scraping tool or API to extract data — such as product listings, article titles, prices, or company details — and then sync that data into Airtable. Once imported, Airtable becomes the system that stores, organises, filters, and refreshes the scraped results.
This guide walks through a simple no-code workflow and shows how to turn Airtable into a live web scraping system.
Can You Scrape a Website into Airtable?
Airtable is not a web scraper itself, but you can scrape a website into Airtable using external tools and APIs.
Typically, you use a scraper such as ParseHub or Apify to extract structured data (headings, prices, images, listings) from a website. Airtable acts as the database that stores, organises, and refreshes the scraped data.
In practice, the scraper exports data as JSON or CSV, and a no-code integration such as Data Fetcher imports it directly into your base. Airtable doesn’t perform the scraping — it manages the results and keeps them up to date on a schedule.
How to a Scrape Website into Airtable
Here’s a simple no-code workflow using ParseHub and Data Fetcher.
1. Create a Scraper in ParseHub
Download and open ParseHub, then click New Project.
Enter the URL you want to scrape (for example, a news page, ecommerce listing, or directory). ParseHub will load the page visually.
Click on the elements you want to extract — such as headings, prices, links, or images. ParseHub automatically detects repeating patterns (like lists of articles or products) and structures them into rows.
Once you’ve selected the fields you need, click Get Data and run the project. ParseHub will generate structured output from the page.
2. Connect ParseHub to Airtable
Install the Data Fetcher extension from the Airtable marketplace.
Create a new request and choose ParseHub as the application. Enter your ParseHub API key to authorise access to your projects.
For the endpoint, select: Import data from a project's latest run
Then choose your ParseHub project and select the Airtable table where the scraped data should be imported.
3. Map Fields and Import Data
Data Fetcher will display the fields returned by ParseHub.
Select the fields you want to import and map them to existing Airtable fields — or allow Data Fetcher to create new fields automatically.
Click Save & Run.
Airtable will now populate with the scraped website data as structured records.
You’ve now scraped website data into Airtable as structured records. From here, you can filter, group, enrich, or automate updates to keep the data current.
Automating Airtable Web Scraping
With Data Fetcher, you can schedule your scraping request to run automatically — for example, every hour or once per day. Each run calls the scraper’s API, imports the latest data, and refreshes your Airtable table.
Use Update write mode in Data Fetcher to merge new scraped data into existing records instead of creating duplicates.
Select one or more unique fields — such as URL — under Update Based on Field(s) in Advanced settings. Data Fetcher will match incoming data to those fields and update the correct records automatically.
How to Schedule Automatic Runs
In Data Fetcher, open your request, enable Schedule, authorise your base if prompted, choose a run frequency (e.g. every 15 minutes, hourly, or daily), and click Save.
From then on, Airtable will automatically sync new scraped data based on your schedule.
Monitoring Your Scraper
Data Fetcher includes a run history log so you can see when each import ran and whether it succeeded, helping ensure your scraper continues working as expected.
With scheduled runs and proper record matching, Airtable becomes a live web scraping system — continuously pulling fresh data into your tables without manual exports or repeated setup.
Common Web Scraping Workflows in Airtable
Once you can scrape websites into Airtable, there are many practical use cases.
Extract Structured Data from URLs
Use AI or APIs to extract titles, descriptions, pricing, or other metadata from a list of URLs. This is useful for SEO audits, content research, or competitor monitoring. Airtable becomes a structured repository of extracted page data.
Scrape Amazon Product Data
Use a scraping service such as Apify to collect product details (price, rating, reviews) and sync them into Airtable. This works well for ecommerce research or price tracking.
Enrich LinkedIn Profiles or Companies
Pull structured profile or company data into Airtable using a LinkedIn data API. This replaces manual research with automated enrichment for lead generation or market analysis.
Capture Website Screenshots Automatically
Generate screenshots of URLs using a screenshot API and store them in Airtable. This is useful for monitoring page changes or collecting visual references at scale.
