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Data schemas

Data schemas is a working area, where system and custom data schemas are stored and where new data schemas can be created and existing can be modifid by users.

Data schema is a data structure that is used to structure the incoming data data flowing to the task step as input - Input schema, or and outgoing data Output schema from the task step as output.

Data schemas level of necessity

Different connectors available in the platform do have four differemt level of necessity.

Optional - The connector does not expect specific schema. The required data structure can be achieved by correct configuration. Although the selected connector doesn't require a schema generally, the individual integration task step may need to match the output data structure of preceding task step and use a data schema selected from the repository or create a new input schema. Example: JS mapper connector - This connector can be used without input schema and produce data using the configuration options, nevertheless can also receive data from other source or from another step where it is required to match a specific data structure.

Mandatory - The connector requires mandatory input or output data schema, which must be selected by the user from the existing data schema repository or a new one must be created. Example: CSV Serializer - The connector will fail without structured data.

Required - The connector expects fixed data structure, otherwise, the connector will fail. It is not possible to modify the required schema for the connector or create a new one Example: Base64 to text converter - Required input of data in format base64.

None - The connector does not require data schema and does not allow to process any data input. Example: Delay - The connector does not require data schemas as it serves a different purpose. It delays the integration process irrespective of the processed data.

Data schema repository

The Data schemas repository consist of ever growing list of system schemas defined by developers matching the needs of available connectors. Besides the system schemas, there are custom schemas, configured by users to match the integration task configuration needs

  • System schemas - cannot be modified or deleted. Can be used as part of the custom created schemas.

  • Custom schemas - can be modified and deactivated. Only the custom schema view contains the +Add button.

Data schema list

Schema name - Name of the schema suggesting the purpose of the schema. The system schemas names usually refer to connectors they belong to. Custom schemas names are defined by users.

Tasks - Count of the tasks where the respective schema is used.

Steps - Count of the task steps where the respective schema is used.

Endpoints - Count of the endpoints where the respective schema is used.


Click on the data schema line in the data schema list will open a non editable view of the schema displaying its:

Versions oredered descending with the recent version on the top.

Schema columns - Dispalys the schema name, column name, data type and obligation to fill.

Used in - List of tasks, task steps, endpoints, where the respective schema is used.

Create & modify data schemas

  • Schema name - define schema name suggesting the purspose of the schema

  • Schema columns - configure required columns of the data structure

Column name

Data type - There are 2 sets of data types available.

  • Simple - Integer, String, Bool, Base64, Double, JSON

  • Complex - List of existing schemas with their defined columns and data types.

Nullable - Defines if the column value is mandatory or optional and null value is allowed.