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• User is able to specify the Key Field based on the Input Schema (has to be field in Input Schema). This the key of the output row. From the above example it’s “Key Field”

• User is able to specify the list of fields that should be considered to form a denormalized record. From the above example it should be ‘FIRST_NAME’, ‘LAST_NAME’, ‘ADDRESS’ & ‘CITY’

• Users are able to specify the output field name for each through mapping. From the above example ‘ADDRESS’ in input is mapped to ‘addr’ in output schema.

• Similarly simple type conversions should be attempted - {int, long, float, double} -> string

Design

Examples

Properties:

• datasetName: name of the database table to be de-normalized.
• keyField: key on the basis of which input record will be de-normalized. This field should be included in input schema.
• outputFieldSchema: list of the fields and its mappings to be included in de-normalized output. For example, ADDRESS (in input) to Addr (in output).

Example:

{

    "name": "RowDenormaliser",

    "type": "transform",

            "properties":

              {

                "keyField" : "",

                "datasetName" : "",

                 "outputFieldSchema": " {..output table schema ...}",

                  "inputSchema": "{.. input table schema..}",

              }

}

The transform takes DataBase table as input record that has a 'KeyField' field (column name) specified by user, de-normalizes it on the basis of this field, and then returns a de-normalised table according to the output schema specified by the user.

 For example, if it receives as an input record:

it will transform it to this output record on the basis of Key Field value "joltie" :