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Release 6.4.2



This example shows how you can cross-reference columns of data using the following transforms:

  • flatten - Flatten values in an array into separate rows in the dataset. See Flatten Transform.
  • valuestocols - Extract unique instances of values into separate columns, with an indicator added to each row where the unique value is found. See Valuestocols Transform.


The following data covers magazine subscriptions for individual customers. Their subscriptions are stored in an array of values. You are interested in who is subscribing to each magazine. 

Anne Aimes["Little House and Garden","Sporty Pants","Life on the Range"]
Barry Barnes["Sporty Pants","Investing Smart"]
Cindy Compton["Cakes n Pies","Powerlifting Plus","Running Days"]
Darryl Diaz["Investing Smart","Cakes n Pies"]


When this data is loaded into the Transformer, you might need to apply a header to it. If it is in CSV format, you might need to apply some replace transforms to clean up the Subscriptions column so it looks like the above. 

When the Subscriptions column contains cleanly formatted arrays, the column is re-typed as Array type. You can then apply the flatten transform:


flatten col:Subscriptions

Each CustId/Subscription combination is now written to a separate row. You can use this new data structure to break out instances of magazine subscriptions. Using the following transform, you can add the corresponding CustId value to the column:

valuestocols col:Subscriptions value:CustId

Delete the two source columns:

drop col:CustId,Subscriptions


Anne Aimes      
 Anne Aimes     
  Anne Aimes    
 Barry Barnes     
   Barry Barnes   
    Cindy Compton  
     Cindy Compton 
      Cindy Compton
   Darryl Diaz   
    Darry Diaz  



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