Snowflake Data Cloud Connector > Part II: Data Integration with Snowflake Data Cloud Connector > Mappings for Snowflake Data Cloud > Mappings in advanced mode example
  

Mappings in advanced mode example

You work for a retail company that offers more than 50,000 products and the stores are distributed across the globe. The company ingests a large amount of customer engagement details from the transactional CRM system into Amazon S3.
The sales team wants to improve customer engagement and satisfaction at every touch point. To create a seamless customer experience and deliver personalized service across the various outlets, the retail company plans to load the data that is stored in the parquet file format from the Amazon S3 bucket to Snowflake.
You can create a mapping in advanced mode to read data from the Amazon S3 bucket and write data to the Snowflake target. You can choose to add transformations in the mapping to process the raw data that you read from the Amazon S3 bucket and then write the curated data to Snowflake.
The following example illustrates how to create a mapping to read from an Amazon S3 source and write to Snowflake:
The mapping includes an Amazon S3 source, Expression transformation, and Snowflake target.
    1In Data Integration, click New > Mappings > Mapping.
    2In the Mapping Designer, click Switch to Advanced.
    The following image shows the Switch to Advanced button in the Mapping Designer:
    In the Mapping Designer, the header includes the Switch to Advanced button.
    3In the Switch to Advanced dialog box, click Switch to Advanced.
    The Mapping Designer updates the mapping canvas to display the transformations and functions that are available in advanced mode.
    4 Enter a name, location, and description for the mapping.
    5Add a Source transformation, and specify a name and description in the general properties.
    6On the Source tab, perform the following steps to read data from the Amazon S3 source:
    1. aIn the Connection field, select the Amazon S3 V2 connection.
    2. bIn the Source Type field, select single object as the source type.
    3. cIn the Object field, select the parquet file object that contains the customer details.
    4. dIn the Advanced Properties section, specify the required parameters.
    The following image shows the configured Source transformation properties that reads customer engagement details from the Amazon S3 object:
    You can view the Amazon S3 source configured properties.
    7On the Expression tab, define an expression to change the file name port of the customer parquet file to uppercase based on your business requirement before you write data to the Snowflake target:
    The following image shows the configured Expression transformation properties:
    Specify the expression for the Amazon S3 input fields before writing the data to the Snowflake target.
    8Add a Target transformation, and specify a name and description in the general properties.
    9On the Target tab, specify the details to write data to Snowflake:
    1. aIn the Connection field, select the Snowflake Data Cloud target connection.
    2. bIn the Target Type field, select single object.
    3. cIn the Object field, select the Snowflake object to which you want to write the curated customer engagement data.
    4. dIn the Operation field, select the insert operation.
    5. eIn the Advanced Properties section, specify the required advanced target properties.
      6. The following image shows the configured Snowflake Target transformation properties:
      You can view the Snowflake target configured properties.
    10Click Save > Run to validate the mapping.
    In Monitor, you can monitor the status of the logs after you run the task.