DocumentationExperience PlatformData Science Workspace Guide

Create the retail sales schema and dataset

Last update: Mon Aug 05 2024 00:00:00 GMT+0000 (Coordinated Universal Time)

CREATED FOR:

  • User
  • Developer
NOTE
Data Science Workspace is no longer available for purchase.
This documentation is intended for existing customers with prior entitlements to Data Science Workspace.

This tutorial provides you with the prerequisites and assets required for all other Adobe Experience Platform Data Science Workspace tutorials. Upon completion, the Retail Sales schema and datasets will be available for you and members of your organization on Experience Platform.

Getting started

Before starting this tutorial, you must have the following prerequisites:

Create Retail Sales schema and dataset

The Retail Sales schema and datasets are created automatically by using the provided bootstrap script. Follow the steps below in order:

Configure files

  1. Inside the Experience Platform tutorial resource package, navigate into the directory bootstrap, and open config.yaml using an appropriate text editor.

  2. Under the Enterprise section, input the following values:

    code language-yaml 
    Enterprise:
    api_key: {API_KEY}
    org_id: {ORG_ID}
    tech_acct: {technical_account_id}
    client_secret: {CLIENT_SECRET}
    priv_key_filename: {PRIVATE_KEY}

  3. Edit the values found under the Platform section, Example shown below:

    code language-yaml 
    Platform:
    platform_gateway: https://platform.adobe.io
    ims_token: {ACCESS_TOKEN}
    ingest_data: "True"
    build_recipe_artifacts: "False"
    kernel_type: Python
    • platform_gateway: The base path for API calls. Do not modify this value.
    • ims_token: Your {ACCESS_TOKEN} goes here.
    • ingest_data: For the purpose of this tutorial, set this value as "True" in order to create the Retail Sales schemas and datasets. A value of "False" will only create the schemas.
    • build_recipe_artifacts: For the purpose of this tutorial, set this value as "False" to prevent the script from generating a Recipe artifact.
    • kernel_type: The execution type of the Recipe artifact. Leave this value as Python if build_recipe_artifacts is set as "False", otherwise specify the correct execution type.

  4. Under the Titles section, provide the following information appropriately for the Retail Sales sample data, save and close the file after edits are in place. Example shown below:

    code language-yaml 
    Titles:
    input_class_title: retail_sales_input_class
    input_mixin_title: retail_sales_input_mixin
    input_mixin_definition_title: retail_sales_input_mixin_definition
    input_schema_title: retail_sales_input_schema
    input_dataset_title: retail_sales_input_dataset
    file_replace_tenant_id: DSWRetailSalesForXDM0.9.9.9.json
    file_with_tenant_id: DSWRetailSales_with_tenant_id.json
    is_output_schema_different: "True"
    output_mixin_title: retail_sales_output_mixin
    output_mixin_definition_title: retail_sales_output_mixin_definition
    output_schema_title: retail_sales_output_title
    output_dataset_title: retail_sales_output_dataset

Run the bootstrap script

  1. Open your terminal application and navigate to the Experience Platform tutorial resource directory.

  2. Set the bootstrap directory as the current working path and run the bootstrap.py Python script by entering the following command:

    code language-bash 
    python bootstrap.py
    note note
    NOTE
    The script may take several minutes to complete.

Next steps

Upon successful completion of the bootstrap script, the Retail Sales input and output schemas and datasets can be viewed on Experience Platform. See the preview schema data tutorial
for more information.

You have also successfully ingested Retail Sales sample data into Experience Platform using the provided bootstrap script.

To continue working with the ingested data:

recommendation-more-help
cc79fe26-64da-411e-a6b9-5b650f53e4e9