Data Cleansing API
Use this REST call to cleanse a dataset by using the pre-defined JSON format. You can include as many input rows as required for the particular dataset.
The call results in cleansing of all input rows and the output is returned in a single response output. The output includes a header, the list of input rows cleansed, MESSAGE, and The output also includes an additional row, MESSAGE, which includes the information about the success or failure of the cleansing API call. In case of failure, the reasons for the failure are described in detail.
Input Attributes
attributeName | Name of the cleanser used. |
columnName | Name of the column in the dataset. |
inputDataSet | Consisting of the following attributes for each row columns:  name. Column name. value. Value contained within the column. |
pkIdentifier | Unique row identifier. |
Required: | attributeName, columnName, inputDataSet |
Optional: | pkIdentifier |
Output Attributes
attributeName | Name of the cleanser used. |
columnName | Name of the column in the dataset. |
Success Response
Error Response
Syntax for Data Cleansing API Request
{
"dqConfig" : {
"additionalConfig" : {
"<key1>":"<value1>",
"<key1>":"<value1>"
}
},
"inputAttributes" : [ {
"attributeName" : ""
"columnName" : ""
}, ........... ],
"outputAttributes" : [ {
"attributeName" : ""
"columnName" : ""
}, ........... ],
"inputDataSet" : {
"rows" : [ {
"columns" : [ {
"name" : "<name of column >",
"value" : "<Value>"
}, ........ ],
"pkIdentifier" : ""
},...........]
}
}
Example for Data Cleansing API Request
{
"dqConfig" : {
"additionalConfig" : {
}
},
"inputAttributes" : [ {
"attributeName" : "IN_Organization",
"columnName" : "Organization"
}, {
"attributeName" : "IN_Address",
"columnName" : "Address"
}, {
"attributeName" : "IN_PostalCode",
"columnName" : "PostalCode"
}, {
"attributeName" : "IN_Country",
"columnName" : "Country"
} ],
"outputAttributes" : [ {
"attributeName" : "OUT_Organization",
"columnName" : "CleansedOrganization"
}, {
"attributeName" : "OUT_Address",
"columnName" : "CleansedAddress"
}, {
"attributeName" : "OUT_PostalCode",
"columnName" : "CleansedPostalCode"
}, {
"attributeName" : "OUT_Locality",
"columnName" : "CleansedCity"
} ],
"inputDataSet" : {
"rows" : [ {
"columns" : [ {
"name" : "Organization",
"value" : "SoftwareAg"
}, {
"name" : "Address",
"value" : "300 Concord Road"
}, {
"name" : "PostalCode",
"value" : "01821"
}, {
"name" : "Country",
"value" : "US"
} ],
"pkIdentifier" : "111"
},
{
"columns" : [ {
"name" : "Organization",
"value" : "SAG"
}, {
"name" : "Address",
"value" : "433 Hakensack Avenue"
}, {
"name" : "PostalCode",
"value" : "07601"
}, {
"name" : "Country",
"value" : "US"
} ],
"pkIdentifier" : "222"
},
{
"columns" : [ {
"name" : "Organization",
"value" : "Software"
}, {
"name" : "Address",
"value" : "1246 Everett Avenue"
}, {
"name" : "PostalCode",
"value" : "40204"
}, {
"name" : "Country",
"value" : "US"
} ],
"pkIdentifier" : "333"
} ]
}
}
Example for Data Cleansing API Output
TBD
