This document discusses integration patterns in Mule ESB, including common patterns such as migration, broadcast, aggregation, bi-directional synchronization, and correlation. The migration pattern allows moving data from one system to another. The broadcast pattern moves data from a single source to multiple destinations. The aggregation pattern extracts and merges data from multiple systems into one. The bi-directional synchronization pattern maintains consistent, real-time data across multiple systems. These patterns are useful for designing integration solutions.

1. Integration Patterns in MULE ESBIntegration Patterns in MULE ESB

2. Architectural stylesArchitectural styles
Service oriented architecture (SOA)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 PatternsIntegration 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 patternMigration 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 patternBroadcast 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 patternAggregation 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 patternBi-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. EndEnd
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