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ai_data_validation [2025/05/25 20:07] – [2. Threshold-based Validation] eagleeyenebulaai_data_validation [2025/05/25 20:10] (current) – [Extensions & Best Practices] eagleeyenebula
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 For structured datasets, integrate JSON schema validation using libraries like `jsonschema`: For structured datasets, integrate JSON schema validation using libraries like `jsonschema`:
  
-```python+<code> 
 +python
 import jsonschema import jsonschema
 from jsonschema import validate from jsonschema import validate
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             logging.error(f"Schema validation failed: {err}")             logging.error(f"Schema validation failed: {err}")
             return False             return False
 +</code>
 # Sample JSON and Schema # Sample JSON and Schema
 +<code>
 data = {"name": "John", "age": 30} data = {"name": "John", "age": 30}
 schema = { schema = {
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 if JsonSchemaValidation.validate(data, schema): if JsonSchemaValidation.validate(data, schema):
     print("JSON Schema Validated Successfully")     print("JSON Schema Validated Successfully")
-``` +</code>
- +
---- +
 ===== Extensions & Best Practices ===== ===== Extensions & Best Practices =====
  
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   * Use detailed logging to ensure traceability.   * Use detailed logging to ensure traceability.
   * Modularize validation logic for usability across pipelines.   * Modularize validation logic for usability across pipelines.
- 
---- 
  
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Data Validation** system is both flexible and powerful, enabling basic to advanced data integrity checks. Its integration into web-based systems and extensibility make it an essential component in data pipelines. The **AI Data Validation** system is both flexible and powerful, enabling basic to advanced data integrity checks. Its integration into web-based systems and extensibility make it an essential component in data pipelines.
ai_data_validation.1748203655.txt.gz · Last modified: 2025/05/25 20:07 by eagleeyenebula