MULE-SalesforceIntegration Patterns

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Salesforce Integration Patterns

Enterprise systems, such as databases, ERP and CRM systemsand custom applications, is critical to enabling sales teams,increasing revenue and better serving customers

The five most common integration patterns are migration, broadcast, aggregation, correlation, and bi-directional synchronization.

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Migration

Data migration is the act of moving a specific set of data at a point in time from one system to another. A migration pattern allows developers to create automated integration services for functionality that will be shared across multiple teams. Developers can set configuration parameters to pass into the API call, so that the migration application can dynamically migrate scoped Salesforce data in or out of Salesforce either on command or in the form of an API. Creating reusable services for frequent migrations can save an exceptional amount of time for development and operations teams

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Broadcast

Broadcast is the act of moving data from a single source system tomany destination systems in an ongoing, near real-time or real-timebasis. Essentially, it is one-way synchronization from one to many.Typically “one way sync” implies a 1:1 relationship; however, thebroadcast pattern can also be a 1:many relationship.

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Providing an External HTTP or HTTPS Port

Aggregation is the act of taking or receiving data from multiplesystems and moving or copying that data into one system. Thealternative would be to run multiple migrations on a daily basis,which means worrying about keeping data accurate, synchronizedand up to date. Broadcasting data from multiple systems

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Providing an External HTTP or HTTPS Port

Bi-directional sync is the act of uniting two or more datasets in two ormore different systems to behave as one system that recognizes theexistence of different datasets. This type of integration is useful whendifferent tools or different systems, which are needed in their ownright and for their own specific purposes, must accomplish differentfunctions on the same data set. Using bi-directional sync to share thedataset enables use of both systems, while maintaining a consistentreal time view of the data across systems.

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