Convert CSV to Schema Markup

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema

By dataclean.to team · 2026-02-13

CSV is the most common format for exporting data from business systems, yet it's the format furthest from what schema.org structured data requires. dataclean.to bridges this gap completely — importing your CSV, detecting data types that the format strips away, cleaning inconsistencies, and mapping columns to schema.org properties to generate validated JSON-LD. Whether you have 50 rows or 50,000, the process transforms raw tabular data into structured markup that search engines can parse, validate, and display as enhanced search results.

The Problem

The fundamental challenge of CSV-to-schema conversion is that CSV is untyped and flat while schema.org is typed and hierarchical. Every value in a CSV is a string, but schema.org properties expect specific types: Number for prices, Date for timestamps, URL for links, and complex nested types like PostalAddress for locations. A single row of product data must become a Product object containing a nested Offer (with price and availability), a nested AggregateRating (with numeric rating and count), and a nested Brand (with name). Building these nested structures from flat CSV columns while ensuring every value is the correct type is the core problem that dataclean.to solves. Schema.org vocabulary specification

How to Fix It

Upload your CSV file

Import any CSV into dataclean.to. The platform handles comma, semicolon, and tab delimiters, various character encodings, and files with thousands of rows. No pre-formatting of column names or values is needed.

Review auto-detected data types

dataclean.to analyzes every column's values (not just headers) to detect data types: prices, dates, URLs, phone numbers, emails, GTINs, ratings, addresses, and more. Correct any misdetections before proceeding.

Clean all data for schema compliance

Apply automatic cleaning: dates normalize to ISO 8601, prices to decimal with currency, phones to E.164, URLs get validated and encoded, text fields get trimmed and sanitized, and special characters get properly escaped for JSON output.

Select schema type and map columns

Choose from 19+ schema types (Product, LocalBusiness, Event, FAQPage, Recipe, JobPosting, Article, Course, Restaurant, and more). Map your CSV columns to the corresponding schema properties. Required properties are highlighted.

Generate and deploy schema markup

Export JSON-LD for all rows. Each CSV row becomes a complete schema.org object with correct @context, @type, and properly nested sub-objects. Deploy via one-line embed script, hosted API, or downloaded files.

Frequently Asked Questions

How many schema types does dataclean.to support?

The platform supports 19+ schema types including Product, LocalBusiness, Event, FAQPage, Article, BlogPosting, NewsArticle, Recipe, JobPosting, Course, Restaurant, Organization, Person, HowTo, Book, MusicRecording, Movie, SoftwareApplication, and TouristAttraction.

Do I need to rename my CSV columns to match schema.org property names?

No. dataclean.to uses a mapping interface where you connect your CSV columns (with whatever names they have) to the corresponding schema.org properties. A column called 'Product Price' maps to offers.price, 'Biz Phone' maps to telephone, and so on.

Can I use the generated schema markup on any website platform?

Yes. JSON-LD markup is a script block that can be added to any HTML page regardless of the CMS or website platform. It works with WordPress, Shopify, custom HTML, Squarespace, Wix, and any other platform that allows adding script tags to page templates.

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.

{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "Cleaned Customer Data",
  "description": "Normalized customer records with standardized fields",
  "keywords": ["customer data", "CRM", "contact list"]
}

💡 How it works: Consistent data formatting reduces import errors and makes your dataset compatible with downstream tools.

Ready to Clean Your Data?

Upload your CSV or spreadsheet and get clean, structured data in minutes.

Get Started Free

Related Use Cases

Schema Generation

Csv To Json Ld

Schema Generation

Csv To Product Schema

Schema Generation

Csv To Rich Snippets

Schema Generation

Airtable To Schema Markup