Hi @MWM , There are couple of issues I noticed regarding the input json and the spec: 1- Regarding the "paycheck" value "3456,98" , I noticed there is coma (,) is that correct? if that is the case , Im not sure the conversion will work in this case.  You have to probably replace the coma with dot (.) and then convert toDouble. It takes few steps to do this in jolt since there is no string replace function using split, join as follows:   , { "operation": "modify-overwrite-beta", "spec": { .... "paycheckArray": "=split('[,]',@(1,paycheck))", "paycheckJoin": "=join('.',@(1,paycheckArray))", "paycheck": "=toDouble(@(1,paycheckJoin))", .... } } , { "operation": "remove", "spec": { "paycheckArray": "", "paycheckJoin": "" } }   2- Regarding the boolean value "isOfAge" , since you have moved it under person object, the spec would as follows:   { "operation": "modify-overwrite-beta", "spec": { ... "person": { "isOfAge": "=toBoolean(@(1,isOfAge))" } .... } }   3- Regarding converting the date value "joiningDate" to number , this is tricky and I dont think you can do it via Jolt, you either have to extract it using EvaluateJsonPath as an attribute then do update attribute using expression language to do the proper formatting and conversion. Since JoltTransformation spec allows expression language , you can reference this attribute in the modify spec. here is an example: https://stackoverflow.com/questions/76678896/jolt-how-to-convert-datetimestamp-to-epoch-on-dynamic-json-keys Another option is to use UpdateRecord to do the proper formatting, conversion for all attributes that require such thing and then just use basic shift spec to re arrange values accordingly. With UpdateRecord you need to use Avro Schema to define the proper types in the JsonRecordSetWriter. UpdateRecord:   JsonRecordSetWriter:   AvroSchema in the Schema Text Property:     { "type": "record", "name": "nifi-record", "fields": [ { "name": "id", "type": "string"}, { "name": "system", "type": "string"}, { "name": "sourceSystem", "type": "string"}, { "name": "name", "type": "string"}, { "name": "lastName", "type": "string"}, { "name": "mail", "type": "string"}, { "name": "country", "type": "string"}, { "name": "city", "type": "string"}, { "name": "street", "type": "string"}, { "name": "building", "type": "string"}, { "name": "local", "type": "string"}, { "name": "paycheck", "type": "double"}, { "name": "joiningDate", "type": { "type":"int", "logicalType":"date" }}, { "name": "isOfAge", "type": ["boolean","null"]} ] }   If you find this helpful please accept solution. Thanks   ... View more

It seems that you need to process the 500 records once. In this case you dont need to start the ExecuteSQL processor nor worry about scheduling it unless you want to run it again in the future. Keep the processor stopped then  right click on it and select Run Once. This should load the total number of records one time. ... View more

Hi @Eva0 , You were close . I think the first astrik "*" was  not needed because the input json is not contained in an array brackets. Then to include the email in each data element it has to be added at the date & type level but to reference the value you need to go to 2 levels up using the @ character as in @(2,mail) where the email is located in relative to date & type. [ { "operation": "shift", "spec": { //level 2 where email originally is "dates": { // level 1 "*": { // start count here at level 0 "@(2,email)": "[&1].email", "date": "[&1].date", "type": "[&1].type" } } } } ] If you find this helpful please accept solution. Thanks ... View more

@Anderosn, Another option is to use the UpdateRecod with an Avro schema in the JsonRecordSetWriter that reflects the actual json structure coming out of the ExecuteSQL The UpdateRecord will look like this: The JsonRecorSetWriter looks like this: The AvroSchema provided in the ShemaText property is the following: { "name": "nifi", "type": "record", "namespace": "nifi.com", "fields": [ { "name": "SEARCH_RESULT", "type": { "name": "SEARCH_RESULT", "type": "record", "fields": [ { "name": "requestId", "type": "string" }, { "name": "responseData", "type": { "name": "responseData", "type": "array", "items": { "name": "responseData", "type": "record", "fields": [ { "name": "searchKey", "type": "string" }, { "name": "data", "type": { "name": "data", "type": "array", "items": { "name": "data", "type": "record", "fields": [ { "name": "firstName", "type": "string" }, { "name": "lastName", "type": "string" } ] } } } ] } } } ] } } ] } This will produce the following output json out of the UpdateRecord: [ { "SEARCH_RESULT" : { "requestId" : "203680", "responseData" : [ { "searchKey" : "cardNumber", "data" : [ { "firstName" : "Martin", "lastName" : "Garry" }, { "firstName" : "Martin", "lastName" : "Garry" }, { "firstName" : "Martin", "lastName" : "Garry" } ] } ] } } ]   You can use EvaluateJsonPath to get the data as json array to do the needed processing. If that helps please accept solution. Thanks           ... View more