Default Guardrails for Real-Time Customer Data Platform B2B Edition
- Topics:
- Guardrails
- B2B
CREATED FOR:
- Admin
Real-Time Customer Data Platform B2B Edition enables you to deliver personalized cross-channel experiences based on behavioral insights and customer attributes in the form of Real-Time Customer Profiles and Account Profiles. To support this new approach to profiles, Experience Platform uses a highly denormalized hybrid data model that differs from the traditional relational data model.
This document provides default use and rate limits to help you model your data for optimal system performance. When reviewing the following guardrails, it is assumed that you have modeled the data correctly. If you have questions on how to model your data, please contact your customer service representative.
Limit types
There are two types of default limits within this document:
Data model limits
The following guardrails provide recommended limits when modeling Real-Time Customer Profile data. To learn more about primary entities and dimension entities, see the section on entity types in the Appendix.
Primary entity guardrails
Note: Due to the nature of Experience Platform’s denormalized hybrid data model, most customers do not exceed this limit. For questions about how to model your data, or if you would like to learn more about custom limits, please contact your customer care representative.
Dimension entity guardrails
Data size limits
The following guardrails refer to data size and provide recommended limits for data that can be ingested, stored, and queried as intended. To learn more about primary entities and dimension entities, see the section on entity types in the Appendix.
Primary entity guardrails
Dimension entity guardrails
Segmentation guardrails
The guardrails outlined in this section refer to the number and nature of audiences an organization can create within Experience Platform, as well as mapping and activating audiences to destinations.
Next steps
The limits outlined in this document represent the changes enabled by Real-Time Customer Data Platform B2B Edition. For a complete list of default limits for Real-Time CDP B2B Edition, combine these limits with the general Adobe Experience Platform limits outlined in the guardrails for Real-Time Customer Profile data documentation.
Appendix
This section provides additional details for the limits in this document.
Entity types
The Profile store data model consists of two core entity types: primary entities and dimension entities.
Primary entity
A primary entity, or profile entity, merges data together to form a “single source of truth” for an individual. This unified data is represented using what is known as a “union view”. A union view aggregates the fields of all schemas that implement the same class into a single union schema. The union schema for Real-Time Customer Profile is a denormalized hybrid data model that acts as a container for all profile attributes and behavioral events.
Time-independent attributes, also known as “record data” are modeled using XDM Individual Profile, while time-series data, also known as “event data” is modeled using XDM ExperienceEvent. As record and time-series data is ingested in Adobe Experience Platform, it triggers Real-Time Customer Profile to begin ingesting data that has been enabled for its use. The more interactions and details that are ingested, the more robust individual profiles become.
Dimension entity
While the Profile data store maintaining profile data is not a relational store, Profile permits integration with small dimension entities in order to create audiences in a simplified and intuitive manner. This integration is known as multi-entity segmentation.
Your organization may also define XDM classes to describe things other than individuals, such as stores, products, or properties. These non-XDM Individual Profile schemas are called “dimension entities” (also known as “lookup entities”) and do not contain time-series data. Schemas that represent dimension entities are linked to profile entities through the use of schema relationships.
Dimension entities provide lookup data which aids and simplifies multi-entity segment definitions and must be small enough that the segmentation engine can load the entire data set into memory for optimal processing (fast point lookup).