Upstream Data Mapping

The Upstream Data Mapping framework includes the entities that are essential to the Data Warehouse (DW) data model. The following are the types of available entities:

Relational: Stores structured data in rows and columns, captures essential details about customers, transactions, and products, and forms the backbone of the data model.
XRef: Facilitates cross-referencing between relational tables to ensure accurate data mapping and integration across systems.
Event-driven: Captures and stores records of specific actions or occurrences to provide real-time insights into user behavior, marketing efforts, and communication processes.
Promotional: Stores data on campaigns, discounts, and offers to enable targeted marketing and tracking of promotional performance.

Integration

When you integrate data into the system, you can use either the SFTP method or the API method. Both methods rely on structural mapping to ensure accurate data ingestion and processing. The following table explains the entity mapping and elaborates how to read and interpret the structured entities table for SFTP and API integrations.

Components	SFTP	API
Field Name	Both SFTP and API integrations use the same taxonomy for field names. Consistent field naming ensures smooth integration and data mapping across different systems and methods.
Important	Both API and SFTP integrations require a Primary Key (PK) to maintain data structure and integrity. The PK is a crucial element in defining the schema and ensuring unique identification of each record.
Data Type	Data types must align with our UDM schema. This ensures proper parsing and transformation during the ETL process.	Data types are inherent in the object payloads and are not explicitly required during transmission, as the API handles data serialization and deserialization.
Referential Integrity	Referential integrity is maintained at the ETL level. The system transforms and reconciles data to ensure that key relationships between entities such as customer-to-transaction links are preserved.	The data model supports nested structures to allow complex relationships between entities. Understanding these relationships is essential to correctly map payload objects and ensure data consistency.
Description	The purpose and expected data type of each field or object. This helps users understand the structure and usage of each field or object within the data transfer to ensure smooth integration and accurate data processing.

The Upstream Data Mapping framework includes the entities that are essential to the Data Warehouse (DW) data model. The following are the types of available entities:

Relational: Stores structured data in rows and columns, captures essential details about customers, transactions, and products, and forms the backbone of the data model.
XRef: Facilitates cross-referencing between relational tables to ensure accurate data mapping and integration across systems.
Event-driven: Captures and stores records of specific actions or occurrences to provide real-time insights into user behavior, marketing efforts, and communication processes.
Promotional: Stores data on campaigns, discounts, and offers to enable targeted marketing and tracking of promotional performance.

Integration

Components	SFTP	API
Field Name	Both SFTP and API integrations use the same taxonomy for field names. Consistent field naming ensures smooth integration and data mapping across different systems and methods.
Important	Both API and SFTP integrations require a Primary Key (PK) to maintain data structure and integrity. The PK is a crucial element in defining the schema and ensuring unique identification of each record.
Data Type	Data types must align with our UDM schema. This ensures proper parsing and transformation during the ETL process.	Data types are inherent in the object payloads and are not explicitly required during transmission, as the API handles data serialization and deserialization.
Referential Integrity	Referential integrity is maintained at the ETL level. The system transforms and reconciles data to ensure that key relationships between entities such as customer-to-transaction links are preserved.	The data model supports nested structures to allow complex relationships between entities. Understanding these relationships is essential to correctly map payload objects and ensure data consistency.
Description	The purpose and expected data type of each field or object. This helps users understand the structure and usage of each field or object within the data transfer to ensure smooth integration and accurate data processing.

The Upstream Data Mapping framework includes the entities that are essential to the Data Warehouse (DW) data model. The following are the types of available entities:

Relational: Stores structured data in rows and columns, captures essential details about customers, transactions, and products, and forms the backbone of the data model.
XRef: Facilitates cross-referencing between relational tables to ensure accurate data mapping and integration across systems.
Event-driven: Captures and stores records of specific actions or occurrences to provide real-time insights into user behavior, marketing efforts, and communication processes.
Promotional: Stores data on campaigns, discounts, and offers to enable targeted marketing and tracking of promotional performance.

Integration

Components	SFTP	API
Field Name	Both SFTP and API integrations use the same taxonomy for field names. Consistent field naming ensures smooth integration and data mapping across different systems and methods.
Important	Both API and SFTP integrations require a Primary Key (PK) to maintain data structure and integrity. The PK is a crucial element in defining the schema and ensuring unique identification of each record.
Data Type	Data types must align with our UDM schema. This ensures proper parsing and transformation during the ETL process.	Data types are inherent in the object payloads and are not explicitly required during transmission, as the API handles data serialization and deserialization.
Referential Integrity	Referential integrity is maintained at the ETL level. The system transforms and reconciles data to ensure that key relationships between entities such as customer-to-transaction links are preserved.	The data model supports nested structures to allow complex relationships between entities. Understanding these relationships is essential to correctly map payload objects and ensure data consistency.
Description	The purpose and expected data type of each field or object. This helps users understand the structure and usage of each field or object within the data transfer to ensure smooth integration and accurate data processing.

Integration

Integration

Integration

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