Data transformations are the daily business in ETL and ESB scenarios. If you have a service- or business process boundary it is very likely that data has to be transformed between different representation.
\nTypical integration scenarios have to deal with a huge amount of different formats (flat file, xml, csv, json, even binary). To make things even worse they tend to change from time to time. Therefore it is important to have tools that are flexible and easy to understand. I found Groovy to be very handy for the transformation job. Let’s have a look at a typical example. Assume we have a flat file in the following format:<\/p>\n
\r\nId Product Amount\r\n 1 Cheese 15\r\n 2 Sausage 2\r\n 3 Bread 3\r\n 4 Butter 4\r\n<\/pre>\nWhat we want is an xml representation like this:<\/p>\n
\r\n\r\n - \r\n
\r\n Cheese<\/name>\r\n 15<\/amount>\r\n <\/item>\r\n - \r\n
\r\n Sausage<\/name>\r\n 2<\/amount>\r\n <\/item>\r\n - \r\n
\r\n Bread<\/name>\r\n 3<\/amount>\r\n <\/item>\r\n - \r\n
\r\n Butter<\/name>\r\n 4<\/amount>\r\n <\/item>\r\n<\/order>\r\n<\/pre>\n The groovy script below does the job. The great thing is not only that is works but also that its easy to develop and maintain. A developer can see the source and target structure at the same time.<\/p>\n
\r\ndef reader = new StringReader(flatfile);\r\ndef writer = new StringWriter();\r\ndef builder = new groovy.xml.MarkupBuilder(writer)\r\n\r\nbuilder.order(){\r\n reader.eachLine() {\r\n def line = it\r\n item(){ \r\n id(id:line.getAt(0..1).trim())\r\n name(line.getAt(3..14).trim())\r\n amount(line.substring(15).trim())\r\n }\r\n }\r\n}\r\n\r\nprintln writer.toString();\r\n<\/pre>\nThanks to closures an the MarkupBuilder class the transformation can be written in a fluent and intuitive way. Typical other transformations are as simple as that, making Groovy a first choice for data transformations.<\/p>\n","protected":false},"excerpt":{"rendered":"
Data transformations are the daily business in ETL and ESB scenarios. If you have a service- or business process boundary it is very likely that data has to be transformed between different representation. Typical integration scenarios have to deal with a huge amount of different formats (flat file, xml, csv, json, even binary). To make … Continue reading Beautiful Transformations with Groovy<\/span><\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9,3],"tags":[11],"class_list":["post-954","post","type-post","status-publish","format-standard","hentry","category-bpm","category-soa","tag-groovy"],"_links":{"self":[{"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/954","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=954"}],"version-history":[{"count":37,"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/954\/revisions"}],"predecessor-version":[{"id":1101,"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/954\/revisions\/1101"}],"wp:attachment":[{"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=954"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=954"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pleus.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=954"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}