anchor.diff:  {transform}. 
   +
   --
  -.. Supply a job ID and the name of the target (or _destination_) index.
  +.. Supply a job ID and the name of the target (or _destination_) index. If the
  +target index does not exist, it will be created automatically.
   
   .. Decide whether you want the {transform} to run once or continuously.
   --
  
positive.code: consideration. What are the most interesting ways you can transform and
  interpret this data?
  
  _Pivoting_ your data involves using at least one field to group it and applying
  at least one aggregation. You can preview what the transformed data will look
  like, so go ahead and play with it!
  
  For example, go to *Machine Learning* > *Data Frames* in {kib} and use the
  wizard to create a {transform}:
  
  [role="screenshot"]
  
negative.code: . Play with various options for grouping and aggregating the data. 
  +
  --
  _Pivoting_ your data involves using at least one field to group it and applying
  at least one aggregation. You can preview what the transformed data will look
  like, so go ahead and play with it!
  
  For example, you might want to group the data by product ID and calculate the
  total number of sales for each product and its average price. Alternatively, you
  might want to look at the behavior of individual customers and calculate how
  