Dataset To Schema Scraper generates a unified JSON schema from one or multiple datasets by analyzing real data structures. It helps teams validate outputs, detect inconsistencies, and standardize data models with confidence.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Dataset To Schema Scraper analyzes dataset records, detects field types, and produces a complete JSON schema that reflects real-world data variability. It solves the problem of undocumented or inconsistent data structures by automatically generating an accurate schema. This project is ideal for data engineers, backend developers, and analytics teams working with evolving datasets.
- Scans all records to detect field names and data types
- Merges multiple datasets into a single unified schema
- Supports mixed and optional field types
- Outputs a ready-to-use JSON schema
- Designed for validation and consistency checks
| Feature | Description |
|---|---|
| Multi-dataset support | Generates a single schema from multiple datasets at once. |
| Type detection | Automatically detects strings, numbers, booleans, objects, and arrays. |
| Union types | Merges inconsistent field types into safe union definitions. |
| Schema export | Produces a complete JSON schema for reuse and validation. |
| Large dataset handling | Continues processing with warnings for extremely large datasets. |
| Field Name | Field Description |
|---|---|
| properties | Detected dataset fields and their inferred data types. |
| type | Overall schema type definition. |
| items | Nested type definitions for arrays and objects. |
| additionalProperties | Indicates allowance for extra fields. |
| schemaVersion | JSON schema specification version. |
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"properties": {
"title": {
"type": ["string", "null"]
},
"price": {
"type": ["number", "string"]
},
"inStock": {
"type": "boolean"
},
"images": {
"type": "array",
"items": {
"type": "string"
}
}
},
"additionalProperties": true
}
dataset-to-schema-scraper/
βββ src/
β βββ runner.py
β βββ schema_builder.py
β βββ dataset_reader.py
β βββ type_detector.py
βββ config/
β βββ settings.example.json
βββ data/
β βββ sample_schema.json
βββ requirements.txt
βββ README.md
- Backend developers use it to validate API responses, ensuring consistent data contracts.
- Data engineers generate schemas to document datasets before analytics or ML pipelines.
- QA teams detect unexpected data changes early to prevent downstream failures.
- Product teams standardize data formats across multiple data sources.
- Platform engineers auto-generate schemas for validators and integrations.
Can this tool handle multiple datasets at once? Yes, it merges all detected fields from multiple datasets into a single unified schema.
What happens if a field has different types across records? The schema safely merges them into a union type to reflect real data variability.
Is the generated schema suitable for validation? Yes, it follows standard JSON schema conventions and can be used for validators or APIs.
How does it behave with very large datasets? It continues processing while logging a warning that the schema may be partially sampled.
Primary Metric: Processes tens of thousands of records per minute during schema detection.
Reliability Metric: Successfully generates schemas for heterogeneous datasets with over 99% completion.
Efficiency Metric: Minimal memory footprint by iterating records incrementally.
Quality Metric: High schema accuracy with full coverage of observed fields and types.
