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@kapa.ai how to bring the date field inside objects, how to add the transformation and bring the date field inside objects

To bring the date field inside the objects, you can use the

AddFields

transformation in Airbyte. This transformation allows you to add fields to an output record, including nested paths. Here's an example of how you can add a field:

yaml stream:     <...>     transformations:         - type: AddFields           fields:             - path: [ "field1" ]               value: "static_value"

In your case, you would replace

"field1"

with

"date"

and

"static_value"

with the date value you want to add. The date value can be a static value or it can be evaluated from the stream slice using the

{{ stream_slice[ 'start_date' ] }}

interpolation placeholder. Here's an example of how you can add a date field evaluated from the stream slice:

yaml stream:     <...>     transformations:         - type: AddFields           fields:             - path: [ "date" ]               value: { { stream_slice[ 'date' ] } }

Please note that the

AddFields

transformation should be added to the

transformations

section of your stream configuration. For more information, you can refer to the [Airbyte documentation on adding fields](https://docs.airbyte.com/connector-development/config-based/understanding-the-yaml-file/record-selector#adding-fields).

@kapa.ai transformations: - type: AddFields fields: path: [ "date" ] value: { { stream_slice['date'] } } getting some error here

A message from kapa.ai

I'm sorry to hear that you're encountering an error. However, without more specific details about the error message or the context in which it's occurring, it's challenging to provide a precise solution. The provided transformation configuration seems to be correct syntactically, assuming that

stream_slice['date']

is a valid expression in your context. Please ensure that: 1. The

stream_slice['date']

is correctly defined and accessible in your context. 2. The indentation in your YAML file is correct. YAML files are sensitive to indentation and a wrong indent can cause errors. If you're still encountering issues, I would recommend checking the logs for more detailed error messages, which can provide more insight into what might be going wrong. For more complex transformations or troubleshooting, you might want to consider using a more advanced transformation tool like dbt. Airbyte has built-in support for dbt (data build tool), which allows you to define custom SQL-based transformations on your data. For more information, you can refer to the [Airbyte documentation on adding fields](https://docs.airbyte.com/connector-development/config-based/understanding-the-yaml-file/record-selector#adding-fields) and [Airbyte's dbt guide](https://docs.airbyte.com/tutorials/transformation-and-normalization/transformations-with-dbt).

it says missed comma between flow collection entries @kapa.ai

A message from kapa.ai

The error message "missed comma between flow collection entries" typically indicates a syntax error in your YAML file. YAML uses specific punctuation and indentation to denote structure, and missing or misplaced characters can cause errors. In your case, the error might be due to the incorrect placement of the curly braces

{}

or the lack of a space after the colon

:

. Here's the corrected version of your transformation:

yaml transformations:   - type: AddFields     fields:       - path: ["date"]         value: "{{ stream_slice['date'] }}"

Please ensure that there's a space after the colon

:

and the curly braces

{}

are correctly placed. For more information, you can refer to the [Airbyte documentation on adding fields](https://docs.airbyte.com/connector-development/config-based/understanding-the-yaml-file/record-selector#adding-fields).

the datatype are stored as array, how to change to other formats @kapa.ai

A message from kapa.ai

In Airbyte, the handling of data types, including arrays, is largely dependent on the destination connector. Destinations must handle all types, but they are free to cast types to a convenient representation. For example, if a destination cannot natively handle

array

fields, the destination connector is free to simply JSON-serialize the array back to a string when pushing data into the end platform. Here's an example of how a source schema with an array type might be handled: Source schema:

json {     "type": "object",     "properties": {       "appointments": {         "type": "array",         "items": {           "type": "string",           "format": "date-time",           "airbyte_type": "timestamp_with_timezone"         }       }     }   }

Destination schema (after transformation):

json {     "type": "object",     "properties": {       "appointments": {         "type": "string"       }     }   }

In this example, the

array

type is cast to a

string

type in the destination schema. For more complex transformations, you might want to consider using a more advanced transformation tool like dbt. Airbyte has built-in support for dbt (data build tool), which allows you to define custom SQL-based transformations on your data. For more information, you can refer to the [Airbyte documentation on supported data types](https://docs.airbyte.com/understanding-airbyte/supported-data-types#unsupported-types) and [Airbyte's dbt guide](https://docs.airbyte.com/tutorials/transformation-and-normalization/transformations-with-dbt).