The document discusses common integration patterns used in service-oriented architectures (SOA). It describes five main patterns: migration, broadcast, aggregation, bi-directional synchronization, and correlation. The migration pattern allows automated migration of data from one system to another. The broadcast pattern disseminates data from a single source to multiple destination systems. The aggregation pattern collects data from multiple systems and consolidates it into a single system. The bi-directional synchronization pattern maintains consistent and up-to-date data across two systems.

1. MULE ESB: Integration Patterns
 Name: Rajesh

2. Architectural styles
Service oriented architecture (SOA)
An architectural style that supports service-orientation.
Service-orientation is a
way of thinking in terms of services and service-based
development and the
outcomes of services.
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3. Integration Patterns
 Existing integration solutions are often modeled after either
business processes/functionalities or data flows
– A combination of both is not common and could be
difficult to design and implement
 Most integration solutions’ architecture can be deducted into just
a few common patterns
– Migration
– Broadcast
– Aggregation
– Bi-directional synchronization
– Correlation
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4. Migration pattern
 Most common pattern
 Data migration is moving a specific set of data at a particular
point in time from one system to another
 Migration pattern allows developers to build automated migration
services that create functionality to be shared across numerous
teams in an organization
 Implementation can be record-by-record or in batch
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System A System B

5. Broadcast pattern
 Data moved/sent from a single source to multiple destination
systems
 Most common use case is keeping data up-to-date between
multiple systems
 Usually implemented as one-way synchronization from one to
many
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System A System C
System D
System B

6. Aggregation pattern
 Takes or receives data from multiple systems and copies or moves
it into just one system
 Enables the extraction and processing of data from multiple
systems and merging them into one application
–
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System DSystem B
System C
System A

7. Bi-directional synchronization pattern
 Unites multiple datasets in multiple different systems, causing them
to behave as one system while allowing them to recognize the
existence of different datasets
 Enables both systems to be used and maintains a consistent
realtime view of the data across systems
 This integration patterns is advantageous when object
representations of reality must be comprehensive and
consistent
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System BSystem A

8. End
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