Schema validation is the process of checking data against a predefined schema to ensure that it adheres to the specified structure, format, and constraints. A schema serves as a blueprint for the data, defining the expected data types, relationships, and rules that the data must follow. By validating data against a schema, errors and inconsistencies can be detected early in the data processing pipeline, preventing issues downstream.

Schema validation is commonly used in various data-related processes, such as data transformation, data integration, and data exchange. It helps maintain data quality and consistency by enforcing rules and standards on incoming data. Additionally, schema validation can help improve data interoperability by ensuring that data conforms to a common standard that different systems can understand and process correctly.

Overall, schema validation plays a crucial role in data management and data processing workflows, enabling organizations to ensure the accuracy, reliability, and integrity of their data assets. By validating data against predefined schemas, organizations can minimize data errors, improve data quality, and facilitate seamless data integration and exchange across different systems and platforms.