When Are the Data Catalog, Semantic Layer
Posted: Thu Feb 13, 2025 5:23 am
In fact, a data catalog is a data-layer-bound manifestation of a semantic layer, connected to data and serving analytics and application layers. A data catalog is valuable for the semantic layer because it has the:
Data source location, schema, lineage, and quality metrics of the data assets, mapping relationships between them to create a unified and consistent view of the data in the entire enterprise. This part of laos whatsapp number data the semantic layer is usually referred to as a data catalog. The automated semantic layer leads to an automated data catalog. In the context of the semantic layer, a data catalog can help to define the various data sources and mappings between them, which can be used to create a unified and consistent view of the data in the entire enterprise.
Data dictionary, which defines the data structure, data element names, data types, and data definitions, and anything to do with the technical aspects of data to foster data sharing.
Business glossary, the business logic cousin of the data dictionary. It is a repository of metadata that defines the business terms, metrics, concepts, and rules used within an organization. In the context of the semantic layer, a business glossary creates a common business vocabulary to ensure that business terms are used consistently across different reports and dashboards.
To summarize, the data catalog helps the semantic layer to provide a unified view of data across different data sources while ensuring that data is used consistently. A data catalog focuses on the inventory list of the data assets with its technical attributes (metadata), while the semantic layer is a virtual layer of business logic over data mapping.
Figure Relationship between data catalog, semantic layer, and data warehouse
and Data Warehouse Required?
Here are the situations where companies might need the data catalog, semantic layer, and data warehouse.
Data warehouses combine data from one or more sources, reducing the load on operational systems, tracking historical changes in data, and providing a single source of truth for deriving insights.
If there are multiple business definitions (in big organizations, they most certainly are), you need standardized business terms and metrics.
Data source location, schema, lineage, and quality metrics of the data assets, mapping relationships between them to create a unified and consistent view of the data in the entire enterprise. This part of laos whatsapp number data the semantic layer is usually referred to as a data catalog. The automated semantic layer leads to an automated data catalog. In the context of the semantic layer, a data catalog can help to define the various data sources and mappings between them, which can be used to create a unified and consistent view of the data in the entire enterprise.
Data dictionary, which defines the data structure, data element names, data types, and data definitions, and anything to do with the technical aspects of data to foster data sharing.
Business glossary, the business logic cousin of the data dictionary. It is a repository of metadata that defines the business terms, metrics, concepts, and rules used within an organization. In the context of the semantic layer, a business glossary creates a common business vocabulary to ensure that business terms are used consistently across different reports and dashboards.
To summarize, the data catalog helps the semantic layer to provide a unified view of data across different data sources while ensuring that data is used consistently. A data catalog focuses on the inventory list of the data assets with its technical attributes (metadata), while the semantic layer is a virtual layer of business logic over data mapping.
Figure Relationship between data catalog, semantic layer, and data warehouse
and Data Warehouse Required?
Here are the situations where companies might need the data catalog, semantic layer, and data warehouse.
Data warehouses combine data from one or more sources, reducing the load on operational systems, tracking historical changes in data, and providing a single source of truth for deriving insights.
If there are multiple business definitions (in big organizations, they most certainly are), you need standardized business terms and metrics.