{"type":"doc","content":[{"type":"paragraph","content":[{"text":"In some data integration scenarios, it can be common for us to receive multiple inbound messages from different business partners or from different application systems. When that happens, these messages may have the same meaning, but follow different formats, depending on the external systems, their data content, and different preferences. The ","type":"text"},{"text":"Normalizer ","type":"text","marks":[{"type":"strong"}]},{"text":"data integration pattern is used to process messages that are semantically equivalent but can arrive in different formats. ","type":"text"}]},{"type":"paragraph"},{"type":"mediaSingle","attrs":{"layout":"center"},"content":[{"type":"media","attrs":{"width":732,"id":"d9c3b4ac-5281-4aec-8457-12e58ad1a25f","collection":"contentId-3980460215","type":"file","height":308}}]},{"type":"paragraph"},{"type":"paragraph","content":[{"text":"The ","type":"text"},{"text":"Normalizer","type":"text","marks":[{"type":"em"}]},{"text":" pattern will utilize a ","type":"text"},{"text":"Message Router ","type":"text","marks":[{"type":"em"}]},{"text":"to route the incoming message to the appropriate ","type":"text"},{"text":"Message Translator","type":"text","marks":[{"type":"em"}]},{"text":". This implementation will require that the ","type":"text"},{"text":"Message Router","type":"text","marks":[{"type":"em"}]},{"text":" be able to detect the type of the inbound message. Many messaging applications will equip each message with a type indicator field in the Message Header to simplify this task. ","type":"text"}]},{"type":"paragraph","content":[{"text":"In the above illustration, we can see that we have implemented the Normalizer pattern using a Content-Based Router in conjunction with several Message Translators. ","type":"text"}]},{"type":"paragraph"},{"type":"heading","attrs":{"level":3},"content":[{"text":"Normalizer using XML DSL","type":"text"}]},{"type":"paragraph","content":[{"text":"In this example, we are converting two different XML messages into a common format and then the commonly formatted messages are routed.","type":"text"}]},{"type":"paragraph"},{"type":"codeBlock","attrs":{"language":"xml"},"content":[{"text":"\n \n \n \n \n /partner\n \n \n \n /customer\n \n \n \n \n \n\n\n","type":"text"}]},{"type":"paragraph"},{"type":"heading","attrs":{"level":3},"content":[{"text":"Using Java Bean as the Normalizer:","type":"text"}]},{"type":"paragraph"},{"type":"codeBlock","attrs":{"language":"java"},"content":[{"text":"public class MyNormalizer {\n\n public void employeeToPerson(Exchange exchange, @XPath(\"/employee/name/text()\") String name) {\n exchange.getMessage().setBody(createPerson(name));\n }\n\n public void customerToPerson(Exchange exchange, @XPath(\"/customer/@name\") String name) {\n exchange.getMessage().setBody(createPerson(name));\n }\n\n private String createPerson(String name) {\n return \"\";\n }\n}","type":"text"}]},{"type":"paragraph"}],"version":1}

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