Snowflake Cloud Data Warehouse V2 Connector > Mappings and elastic mappings with Snowflake Cloud Data Warehouse V2 Connector > Elastic mapping example
  

Elastic mapping 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 an elastic mapping that runs on the elastic cluster to achieve faster performance when you read data from the Amazon S3 bucket and write data to the Snowflake target.
In the elastic mapping, you can choose to add transformations 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 simple elastic mapping to read from an Amazon S3 source and write to Snowflake:
The mapping includes an Amazon S3 source, Expression transformation, and Snowflake target.
    1. In Data Integration, click New > Mappings > Elastic Mapping > Create.
    The New Mapping dialog box appears.
    2. Enter a name, location, and description for the mapping.
    3. Add a Source transformation, and specify a name and description in the general properties.
    4. On the Source tab, perform the following steps to read data from the Amazon S3 source:
    1. a. In the Connection field, select the Amazon S3 V2 connection.
    2. b. In the Source Type field, select single object as the source type.
    3. c. In the Object field, select the parquet file object that contains the customer details.
    4. d. In 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.
    5. On 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.
    6. Add a Target transformation, and specify a name and description in the general properties.
    7. On the Target tab, specify the details to write data to Snowflake:
    1. a. In the Connection field, select the Snowflake Cloud Data Warehouse V2 target connection.
    2. b. In the Target Type field, select single object.
    3. c. In the Object field, select the Snowflake object to which you want to write the curated customer engagement data.
    4. d. In the Operation field, select the insert operation.
    5. e. In 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.
    8. Click Save > Run to validate the mapping.
    In Monitor, you can monitor the status of the logs after you run the task.