✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-13
Airtable bases contain structured information that maps naturally to schema.org types — product catalogs, event calendars, business directories, course listings, and more. The challenge is that Airtable's collaborative editing and flexible field types produce data that doesn't meet schema.org's strict property requirements. dataclean.to connects to your Airtable account, normalizes the data, and generates validated JSON-LD schema markup that search engines can parse without errors.
The Problem
Schema.org properties have specific data type requirements. A Product's price must be a number, not a formatted string. An Event's startDate must be ISO 8601, not "Next Saturday at 3pm." Airtable fields that look correct to humans — currency-formatted numbers, natural language dates, formula outputs — consistently fail schema validation. Manually reformatting hundreds or thousands of records is not practical, especially when the base is actively updated by multiple collaborators who each have their own data entry habits. Schema.org vocabulary specification
How to Fix It
1
Connect Airtable or upload CSV export
Either connect your Airtable account via OAuth for direct import, or export your base as CSV. Direct connection preserves field types (number, date, currency) that CSV export flattens to plain text.
2
Review auto-detected column types
dataclean.to analyzes each column and identifies its data type. Review the detection results and adjust if needed. Columns detected as prices, dates, URLs, emails, and phone numbers will receive type-specific cleaning rules.
3
Apply data cleaning rules
Clean the data to meet schema.org requirements. This includes normalizing date formats to ISO 8601, converting prices to numeric values with currency codes, validating URLs, standardizing phone number formats, and trimming inconsistent whitespace from text fields.
4
Select schema types and map columns
Choose from 19+ supported schema types including Product, LocalBusiness, Event, FAQPage, Recipe, JobPosting, and more. Map your Airtable columns to the corresponding schema.org properties. Required properties are highlighted to ensure your markup is complete.
5
Generate and deploy schema markup
Export JSON-LD markup for all rows in your dataset. Choose between downloadable files for manual integration, or use the hosted API endpoint with the auto-embed script to serve schema markup dynamically on your website.
Frequently Asked Questions
Which schema types can I generate from Airtable data?
dataclean.to supports 19+ schema types including Product, LocalBusiness, Event, FAQPage, Article, Recipe, JobPosting, Course, Restaurant, Organization, Person, HowTo, and more. Any Airtable base with structured data can be mapped to the appropriate schema type.
Does the OAuth import pull all tables from my Airtable base?
You choose which table and view to import. The OAuth connection requests read-only access to your bases, and you select the specific table during the import flow. Only the data from the selected table and view is imported into dataclean.to.
Can I automate schema generation when Airtable data changes?
After the initial import and mapping, you can re-import your Airtable data to update the schema markup. If you use the hosted API endpoint, the updated data is served immediately. The column mappings and cleaning rules from your previous configuration are preserved.
Example: Input → Output
name
email
phone
city
status
Alice Johnson
alice@example.com
+1-555-0101
New York
active
alice johnson
ALICE@EXAMPLE.COM
5550101
new york
Active
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.