Abstracting data models from implementation with Category Theory gives independent life to data models, underpins data transfers with mathematical rigour and enables dream-like applications.
A unique method and approach to create Abstract Data Model using Category Theory, a branch of Mathematics, an Abstract Data Model that is composable, extensible and enforces structure preserving operations on data with Mathematical guarantee to data quality, provenance reliability and 100% constraint compliance at rest, during ingress, egress and facilitates existing data models of any type or non-data-model based data like IoT sensor data / text to inherit these Category Theoretical properties, including infinite data model composability and metadata extensibility among other properties.
Lack of abstracted separation of the Data Models (schema) is restrictive. Data Models do not have an independent existence outside the databases, as they are locked in the databases at the time of creation (schema). Unintended result of locking the data models in the databases resulted in disparate data models and information silos, the effects of which are severe. This one aspect of locking the Data Models in the native databases without abstraction resulted in immense inefficiencies across several aspects – from application integration to data engineering. This practice left several unintended consequences the entire industry is forced to live with for decades, a few examples:
Read further here: https://surecan.substack.com/p/abstract-composable-extensible-data
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