SVV_INTEGRATION_TABLE_MAPPING - Amazon Redshift
Table columnsSample queries

SVV_INTEGRATION_TABLE_MAPPING

SVV_INTEGRATION_TABLE_MAPPING displays the mapping of source database, schema, table, column, and data type to the target when the identifier value of those fields are different.

Note

This view is only populated for the following types of zero-ETL integrations:

  • AWS Glue third-party applications to Amazon SageMaker Lakehouse

  • Amazon DynamoDB to Amazon SageMaker Lakehouse

For more information, see Zero-ETL integrations in the AWS Glue Developer Guide.

The transformation of identifier values from source to target follow the following rules:

  • An upper case letter is converted to lower case.

  • A character that is not a lowercase letter, a digit, or underscore (_) is converted to an underscore (_).

  • If there is a conflict with a existing identifier value, then an Universally Unique Identifier (UUID) is appended to the new identifier.

  • If the source identifier value is an Amazon Redshift keyword, then the suffix _redshift is appended to the new identifier.

After transformation, a character must be a lowercase letter, a digit, or underscore (_) and match the regex pattern [a-z0-9_]. The following examples demonstrate the conversion rules:

Source Target Notes
foofooNo transformation
Barbar
fooBarfoobar
foo1foo1No transformation
foo_1foo_1No transformation
Bar@1bar_1
foo_bar@foo_bar_
casecase_redshift

SVV_INTEGRATION_TABLE_MAPPING is visible to all users. Superusers can see all rows; regular users can see only their own data. For more information, see Visibility of data in system tables and views.

For information about zero-ETL integrations, see Zero-ETL integrations in the Amazon Redshift Management Guide.

Table columns

Column name Data type Description
integration_id character(128) The identifier associated with the integration.
source_database character(128) The name of the database in the source.
target_database character(128) The database in Amazon Redshift that receives the integration data.
source_schema_name character(128) The name of the schema in the source.
target_schema_name character(128) The schema in Amazon Redshift that receives the integration data.
source_table_name character(128) The name of the table in the source.
target_table_name character(128) The table in Amazon Redshift that receives the integration data.
source_column_name character(128) The name of the column in the source.
target_column_name character(128) The column in Amazon Redshift that receives the integration data.
source_data_type character(128) The data type of the column in the source.
target_data_type character(128) The data type of the column in Amazon Redshift that receives the integration data.

Sample queries

The following SQL command displays mapping of metadata values from source to target.

select * from svv_integration_table_mapping;

           integration_id              | source_database | target_database |  source_schema_name | target_schema_name | source_table_name | target_table_name | 
---------------------------------------+-----------------+-----------------+---------------------+--------------------+---------------------------------------+
  99108e72-1cfd-414f-8cc0-0216acefac77 |     mydatabase  |  mydatabase     |  myschema           | myschema           | Mytable           | mytable           | 
  
  
                                       | source_column_name | target_column_name |  source_data_type | target_data_type | 
                                       +--------------------+--------------------+-------------------+------------------+
                                       | Mycolumnname       | mycolumnname       |  Mydatatype       | mydatatype       |