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Mule integration patterns


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Mule integration patterns

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Mule integration patterns

  1. 1. Integration PatternsIntegration Patterns
  2. 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. 2
  3. 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 3
  4. 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 4 System A System B
  5. 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 5 System A System C System D System B
  6. 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 – 6 System DSystem B System C System A
  7. 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 7 System BSystem A
  8. 8. EndEnd © 2013 Infosys Limited, Bangalore, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.