In the past, exchanging data within and outside a company was relatively simple, especially in a monolithic architecture. However, the rise of distributed architectures has led to an exponential increase in touchpoints, which has led to an exponential increase in data exchange specifications. This duplication of describing the same data in different languages, formats, and types has led to data loss and desynchronization, which has created challenges for data quality .
Schemas, as fundamental elements of data management, provide the basic structure that determines how data is organized and presented. Their widespread use stems from their ability to provide brazil business fax list blueprints to represent and structure information across different domains . A key function of a schema is to establish a structured framework for data by specifying types, relationships, and constraints .
This methodical approach enhances the understandability of data for both human interpretation and machine processing. In addition, schemas promote interoperability by fostering a common understanding of data structure between different systems and platforms , thereby facilitating smooth data exchange and integration. In the field of data operations (dataOps), which is centered around the automation of data-related processes, schemas play a key role in defining the structure of data pipelines.