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• For log data or other transactional data, the timestamp is typically a unique identifier.

  Tip
  Tip: If you think you need a primary identifier for your dataset, you should try to identify it or create it before you drop delete potentially useful columns.

• Product information typically contains an SKU identifier. If that is not available, you may need brand, make, and model combinations, which can be created using the method described below.

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1. Change this column to be of String type. Select String from the data type drop-down for the column.

2. Create a column containing your prepended identifier and the proper number of zeroes. The following bit of logic generates a string with the proper number of zeroes depending on the length of the value in origRowId:
   

   D trans
   p03Value keyPrefix Type step p01Name Formula type p01Value Single row formula p02Name Formula p02Value IF(LEN(origRowId) > 3, 'ORD', IF(LEN(origRowId) > 2, 'ORD0',IF(LEN(origRowId) > 1, 'ORD00','ORD000'))) p03Name New column name SearchTerm New formula

   Info
   NOTE: The following works for up to 10,000 rows in the original dataset. You need to add additional IF clauses when your row counts exceed 10,000.

3. Now, you can merge these columns together:
   

   D trans
   Type step p01Name Columns p01Value keyPrefix,origRowId p02Name New column name p02Value primaryKey SearchTerm Merge columns

4. You can now drop delete the prefix column:
   

   D trans
   Type step p01Name Columns p01Value keyPrefix p02Name Action p02Value Delete selected columns SearchTerm Delete columns

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1. Load the dataset into the Transformer page.
2. Identify the columns that together can uniquely identifier a row. In the TestScores-All example, these columns are the following:
   1. LastName
   2. FirstName

   3. TestNumber

      Info
      NOTE: It may be possible to set up a key using LastName and TestNumber, but that is not guaranteed. If the dataset changes over time, a working key based on these columns may become broken.

3. Use the merge transform to combine these columns together into a new column, such as the following:
   

   D trans
   p03Value TestID Type step p01Name Columns p01Value LastName,FirstName,TestNum p02Name Separator p02Value '-' p03Name New column name SearchTerm Merge columns

   The with clause identifies the delimiter between the merged column values.

4. Values should look like the following:

   TestID
   Smith-Joe-2
   Doe-Jane-4
   Jones-Jack-1

5. In some cases, you may want to drop the delete the source columns for the primary key.