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Sample ETL Transformations

This article demonstrates the following example transformations that an extract, transform, load (ETL) developer can encounter.

Flat CSV to hierarchy

Sample files

Sample CSV and JSON files are available from the public ETL Reference GitHub repo maintained by Adobe:

Example CSV

The following CRM data has been exported as CRM_profiles.csv :
TITLE   F_NAME  L_NAME  GENDER  DOB EMAIL   CRMID   ECID    LOYALTYID   ECID2   PHONE   STREET  CITY    STATE   COUNTRY ZIP LAT LONG
Mr  Ewart   Bennedsen   M   2004-09-25  ebennedsenex@jiathis.com    71a16013-d805-7ece-9ac4-8f2cd66e8eaa    87098882279810196101440938110216748923  2e33192000007456-0365c00000000000   55019962992006103186215643814973128178  256-284-7231    72 Buhler Crossing  Anniston    Alabama US  36205   33.708276   -85.7922905
Dr  Novelia Ansteys F   1987-10-31  nansteysdk@spotify.com  2eeb6532-82e1-0d58-8955-bf97de66a6f5    50829196174854544323574004005273946998  2e3319208000765b-3811c00000000001   65233136134594262632703695260919939885  704-181-6371    79 Northfield Hill  Charlotte   North Carolina  US  28299   35.2188655  -80.8108885
Mr  Ulises  Mochan  M   1996-03-20  umochanco@gnu.org   6f393075-addb-bdd6-73f8-31c393b700f5    70086119428645095847094710218289660855  2e33192080003023-26b2000000000002   82011353387947708954389153068944017636  720-837-4159    00671 Mifflin Trail Lacolle Québec CA  E5A 45.08338    -73.36585
Mrs Friederike  Durrell F   1979-01-3   fdurrellbj@utexas.edu   33d018ec-5fed-f1a3-56aa-079370a9511b    50164729868919217963697788808932473456  2e33192080006dfc-0cdf400000000003   64452712468609735658703639722261004071  798-528-3458    47 Fremont Hill Independencia   Veracruz Llave  MX  91891   19.3803931  -99.1476905
Rev Evita   Bingall F   1974-02-28  ebingallod@mac.com  8c93db88-f328-8efb-dc73-d5654d371cbe    74973364195185450328832136951985519627  2e331920800038db-0559e00000000004   58945501950285346322834356669253860483  397-178-5897    56 Crescent Oaks Court  Buenavista  Oaxaca  MX  71730   19.4458447  -99.1497665
Mr  Eugenie Bechley F   1969-05-19  ebechley9r@telegraph.co.uk  b0c76a3f-6526-0ad0-e050-48143b687d18    67119779213799783658184754966135750376  2e331920800001a4-24b2800000000005   59715249079109455676103900762283358508  718-374-7456    5760 Southridge Junction    Staten Island   New York    US  10310   40.6307451  -74.1181235
Dr  Cammi   Haslen  F   1973-12-17  chaslenqv@ehow.com  56059cd5-5006-ce5f-2f5f-15b4d856a204    61747117963243728095047674165570746095  2e33192080007c25-2ec0600000000006   86268258269066295956223980330791223320  865-538-8291    83 Veith Street Knoxville   Tennessee   US  37995   35.95   -84.05

Mapping

The mapping requirements for the CRM data are outlined in the following table and include the following transformations:
  • Identity columns to identityMap properties
  • Date of Birth (DOB) to Year and Month-Day
  • Strings to Doubles or Short Integers.
CSV Column
XDM Path
Data Formatting
TITLE
person.name.courtesyTitle
Copy as string
F_NAME
person.name.firstName
Copy as string
L_NAME
person.name.lastName
Copy as string
GENDER
person.gender
Transform gender as corresponding person.gender enum value
DOB
person.birthDayAndMonth: "MM-DD"
person.birthDate: "YYYY-MM-DD"
person.birthYear: YYYY
Transform birthDayAndMonth as string
Transform birthDate as string
Transform birthYear as short int
EMAIL
personalEmail.address
Copy as string
CRMID
identityMap.CRMID #
Copy as string to CRMID array in identityMap and set Primary as false
ECID
identityMap.ECID #
Copy as string to first entry in ECID array in identityMap and set Primary as false
LOYALTYID
identityMap.LOYALTYID #
Copy as string to LOYALTYID array in identityMap and set Primary as true
ECID2
identityMap.ECID #
Copy as string to second entry in ECID array in identityMap and set Primary to false
PHONE
homePhone.number
Copy as string
STREET
homeAddress.street1
Copy as string
CITY
homeAddress.city
Copy as string
STATE
homeAddress.stateProvince
Copy as string
COUNTRY
homeAddress.country
Copy as string
ZIP
homeAddress.postalCode
Copy as string
LAT
homeAddress.latitude
Convert to double
LONG
homeAddress.longitude
Convert to double

Output XDM

The following sample shows the first two rows of the CSV transformed to XDM, as shown in CRM_profiles.json :
{
   "person": {
      "name": {
         "courtesyTitle": "Mr",
         "firstName": "Ewart",
         "lastName": "Bennedsen"
      },
      "gender": "male",
      "birthDayAndMonth": "09-25",
      "birthDate": "2004-09-25",
      "birthYear": 2004
   },
   "identityMap": {
      "CRMID": [{
         "id": "71a16013-d805-7ece-9ac4-8f2cd66e8eaa",
         "primary": false
      }],
      "ECID": [{
         "id": "87098882279810196101440938110216748923",
         "primary": false
      },
      {
         "id": "55019962992006103186215643814973128178",
         "primary": false
      }],
      "LOYALTYID": [{
         "id": "2e33192000007456-0365c00000000000",
         "primary": true
      }]
   },
   "homePhone": {
      "number": "256-284-7231"
   },
   "personalEmail": {
      "address": "ebennedsenex@jiathis.com"
   },
   "homeAddress": {
      "street1": "72 Buhler Crossing",
      "city": "Anniston",
      "stateProvince": "Alabama",
      "country": "US",
      "postalCode": "36205",
      "_schema": {
         "latitude": 33.708276,
         "longitude": -85.7922905
      }
   }
},{
   "person": {
      "name": {
         "courtesyTitle": "Dr",
         "firstName": "Novelia",
         "lastName": "Ansteys"
      },
      "gender": "female",
      "birthDayAndMonth": "10-31",
      "birthDate": "1987-10-31",
      "birthYear": 1987
   },
   "identityMap": {
      "CRMID": [{
         "id": "2eeb6532-82e1-0d58-8955-bf97de66a6f5",
         "primary": false
      }],
      "ECID": [{
         "id": "50829196174854544323574004005273946998",
         "primary": false
      },
      {
         "id": "65233136134594262632703695260919939885",
         "primary": false
      }],
      "LOYALTYID": [{
         "id": "2e3319208000765b-3811c00000000001",
         "primary": true
      }]
   },
   "homePhone": {
      "number": "704-181-6371"
   },
   "personalEmail": {
      "address": "nansteysdk@spotify.com"
   },
   "homeAddress": {
      "street1": "79 Northfield Hill",
      "city": "Charlotte",
      "stateProvince": "North Carolina",
      "country": "US",
      "postalCode": "28299",
      "_schema": {
         "latitude": 35.2188655,
         "longitude": -80.8108888
      }
   }
}

Dataframe to XDM schema

The hierarchy of a dataframe (such as a Parquet file) must match that of the XDM schema being uploaded to.

Example dataframe

The structure of the following example dataframe has been mapped to a schema that implements the XDM Individual Profile class, and contains the most common fields associated with schemas of that type.
[
    StructField("person", StructType(
        [
            StructField("name", StructType(
                [
                    StructField("courtesyTitle", StringType()),
                    StructField("firstName", StringType()),
                    StructField("lastName", StringType())
                ]
            )),
            StructField("gender", StringType()),
            StructField("birthDayAndMonth", StringType()),
            StructField("birthDate", StringType()),
            StructField("birthYear", LongType())
        ]
    )),
    StructField("identityMap", MapType(
        StructField("CRMID", ArrayType(
            StructType(
                [
                    StructField("id", StringType()),
                    StructField("primary", BooleanType())
                ]
            )
        )),
        StructField("ECID", ArrayType(
            StructType(
                [
                    StructField("id", StringType()),
                    StructField("primary", BooleanType())
                ]
            )
        )),
        StructField("LOYALTYID", ArrayType(
            StructType(
                [
                    StructField("id", StringType()),
                    StructField("primary", BooleanType())
                ]
            )
        ))
    )),
    StructField("homePhone", StructType(
        [
            StructField("number", StringType())
        ]
    )),
    StructField("personalEmail", StructType(
        [
            StructField("address", StringType())
        ]
    )),
    StructField("homeAddress", StructType(
        [
            StructField("street1", StringType()),
            StructField("city", StringType()),
            StructField("stateProvince", StringType()),
            StructField("country", StringType()),
            StructField("postalCode", StringType()),
            StructField("_schema", StructType(
                [
                    StructField("latitude", DoubleType()),
                    StructField("latitude", DoubleType()),
                ]
            ))
        ]
    ))    
]

When constructing a dataframe for use in Adobe Experience Platform, it is important to ensure that its hierarchical structure is an exact match to that of an existing XDM schema in order for the fields to map properly.

Identities to identity map

Array of identities

[
  {
    "xdm:id": "someone1@example.com",
    "xdm:namespace": {
      "xdm:code": "Email"
    }
  },
  {
    "xdm:id": "2eeb6532-82e1-0d58-8955-bf97de66a6f5",
    "xdm:namespace": {
      "xdm:code": "CRMID"
    }
  },
  {
    "xdm:id": "2e3319208000765b-3811c00000000001",
    "xdm:namespace": {
      "xdm:code": "LOYALTYID"
    }
  }
]

Mapping

The mapping requirements for the array of identities are outlined in the following table:
Identity Field
identityMap Field
Data Type
identities # .id
identityMap Email
copy as string
identities # .id
identityMap CRMID
copy as string
identities # .id
identityMap LOYALTYID
copy as string

Output XDM

Below is the array of identities transformed to XDM:
"identityMap": {
      "Email": [{
         "id": "someone1@example.com"
      }],
      "CRMID": [{
         "id": "2eeb6532-82e1-0d58-8955-bf97de66a6f5"
      }],
      "LOYALTYID": [{
         "id": "2e3319208000765b-3811c00000000001"
      }]
   }