```yaml
product: AlterLab
title: "Schema Validation — Web Scraping Glossary | AlterLab"
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
source_url: https://alterlab.io/glossary/schema-validation
```

extraction

# Schema Validation

Schema validation checks that extracted data conforms to an expected structure and type constraints before it is written to a database or downstream system.

Web pages change without notice, and scraping pipelines can silently produce invalid data when a site restructures its HTML or changes field formats. Schema validation adds a checkpoint after the extraction step: each extracted record is checked against a defined schema (field names, data types, required fields, value ranges) and records that fail validation are quarantined for review rather than written to the destination.

Common schema validation tools for Python include Pydantic (declarative model validation with type coercion), Cerberus, and jsonschema. For TypeScript pipelines, Zod provides runtime schema validation that mirrors TypeScript types.

Schema validation failures are early-warning signals that the target site's structure has changed, allowing the scraping team to update selectors and field mappings before large volumes of bad data accumulate in the destination. Validation reports are a key component of data quality monitoring in production scraping systems.

### What is Schema Validation?

Schema validation checks that extracted data conforms to an expected structure and type constraints before it is written to a database or downstream system.

### How does AlterLab handle Schema Validation?

Schema validation failures are early-warning signals that the target site's structure has changed, allowing the scraping team to update selectors and field mappings before large volumes of bad data accumulate in the destination. Validation reports are a key component of data quality monitoring in production scraping systems.

## Examples

```
from pydantic import BaseModel, HttpUrl
from typing import Optional

class ProductRecord(BaseModel):
    name: str
    price: float
    sku: Optional[str] = None
    url: HttpUrl

# Validate extracted data — raises ValidationError if invalid
record = ProductRecord(**extracted_fields)
```

## Related Terms

[Structured Data ExtractionConverting free-form HTML into typed JSON records using explicit schemas, producing clean structured output instead of raw markup.](https://alterlab.io/glossary/structured-data-extraction)[Data PipelineA data pipeline is an automated sequence of steps that ingests raw data from a source, transforms it, and delivers it to a destination such as a database, data warehouse, or analytics system.](https://alterlab.io/glossary/data-pipeline)[ETL (Extract, Transform, Load)ETL is a data integration pattern where raw data is Extracted from a source, Transformed into the desired format, and Loaded into a destination system.](https://alterlab.io/glossary/etl)[Data EnrichmentData enrichment augments scraped records with additional information from secondary sources — geocoding addresses, looking up company data, or appending social profiles.](https://alterlab.io/glossary/data-enrichment)[JSONJavaScript Object Notation — the universal text format for structured data exchange between APIs and web services.](https://alterlab.io/glossary/json)

## Extract Schema Validation data from any website

AlterLab returns clean, structured data from any public URL — no scraper infrastructure needed. Start free, no credit card required.

[View API docs](https://alterlab.io/docs/api/rest)

[Back to Glossary](https://alterlab.io/glossary)