Can you describe in more detail what you mean by semantic? Doesn’t the DB schema capture the business logic to a large extent?
A semantic data layer is an abstraction layer that sits between the raw data stored in databases and the applications or analytics tools that use this data. The purpose of this layer is to provide a consistent, business-friendly interface to data that might be stored in a variety of formats, tables, or systems. By doing so, the semantic data layer helps in simplifying complex data structures into more meaningful, understandable models. Key Features:
Unified View: It provides a unified view of data from multiple sources, making it easier for users to access and understand the data without having to know the underlying structure or complexity.
Data Governance: It can enforce business rules, security policies, and data quality measures, ensuring that data is consistent, compliant, and accessible only by authorized users.
Flexibility: A semantic layer is often designed to be flexible, allowing business users to adapt to changes in the data model without requiring changes to the applications themselves.
Query Simplification: The layer simplifies the process of querying data by providing a more user-friendly way of accessing and manipulating data, often through a drag-and-drop interface or other graphical tools.
Decoupling: It decouples application development from data source changes. When underlying data changes, you don't necessarily have to update all the applications that use it; you might only need to update the semantic layer.
Data Integration: It can integrate data from multiple sources, providing a single "source of truth" for business users and applications.
Simplification: Make data more accessible and easier to understand for non-technical users.
Consistency: Ensure that everyone is working from the same definitions and business rules.
Efficiency: Reduce the time and complexity involved in generating reports, analytics, and other data-driven functions.
Data Quality: Help to enforce data quality and governance policies.
Security: Provide a mechanism for enforcing security rules on who can see or modify data.
Adaptability: Enable quicker adaptation to changes in business needs or data structures.