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05_​SAS_​SPSS_​MATLAB_​meet_​S3

Workflow

In this example, proprietary files can either be loaded directly into Amazon S3 or alternatively first converted to an open format then uploaded to Amazon S3. The proprietary software is not required to do this.You will need to provide your own Amazon S3 Access Key ID and Secret Access Key for the example to function (see article linked in the description). Connect to S3 using Access Key ID Folders created on root level are created as buckets on S3. The virtualfilesystem functionality is fully supported by the file handling nodes.A bucketand a directory inside that bucket is created. SASSPSSMatlab Loop over eachfile, choosing anappropriate actionbased on file type Create a list of the 6proprietary filesassociated with thisexampleCreate a bucketwith a unique nameCreate directoryinside the created bucketList the all files/foldersin the created examplebucketUpload either- proprietary filesor- CSV FilesChoose option toUpload orconvert and upload Set Option to Delete or keep all files(good for cleaning up)The very first timeyou run this workflow,the R.matlab libraryis installed. That might takeone minute. Enter your own Access Key ID +Security Key SAS7BDAT Reader R Source (Table) R Source (Table) CSV Writer Table Row ToVariable Loop Start CASE SwitchVariable (Start) CSV Writer CSV Writer CASE SwitchVariable (End) Variable Loop End List Files Create S3 Names Create Directory Create UniqueBucket Name Create Directory List Remote Files Upload Choose Load Type End IF String to URI String to URI Delete Bucket R Snippet Amazon S3Connection In this example, proprietary files can either be loaded directly into Amazon S3 or alternatively first converted to an open format then uploaded to Amazon S3. The proprietary software is not required to do this.You will need to provide your own Amazon S3 Access Key ID and Secret Access Key for the example to function (see article linked in the description). Connect to S3 using Access Key ID Folders created on root level are created as buckets on S3. The virtualfilesystem functionality is fully supported by the file handling nodes.A bucketand a directory inside that bucket is created. SASSPSSMatlab Loop over eachfile, choosing anappropriate actionbased on file type Create a list of the 6proprietary filesassociated with thisexampleCreate a bucketwith a unique nameCreate directoryinside the created bucketList the all files/foldersin the created examplebucketUpload either- proprietary filesor- CSV FilesChoose option toUpload orconvert and upload Set Option to Delete or keep all files(good for cleaning up)The very first timeyou run this workflow,the R.matlab libraryis installed. That might takeone minute. Enter your own Access Key ID +Security Key SAS7BDAT Reader R Source (Table) R Source (Table) CSV Writer Table Row ToVariable Loop Start CASE SwitchVariable (Start) CSV Writer CSV Writer CASE SwitchVariable (End) Variable Loop End List Files Create S3 Names Create Directory Create UniqueBucket Name Create Directory List Remote Files Upload Choose Load Type End IF String to URI String to URI Delete Bucket R Snippet Amazon S3Connection

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Nodes

05_​SAS_​SPSS_​MATLAB_​meet_​S3 consists of the following 43 nodes(s):

Plugins

05_​SAS_​SPSS_​MATLAB_​meet_​S3 contains nodes provided by the following 7 plugin(s):