Transformation Configuration Setup

This pages provides instructions on what transform code should be added to a script manifest file to transform Tiktok data into the Unified Marketing Schema (UMS)

Overview

The Tiktok extractor provides data on marketing spend and performance from impressions through vendor attributed revenue and orders. The Daasity transformation code maps data from the Tiktok schema into the Unified Marketing Schema (UMS) for the following marketing channels

Feature Dependencies

You must have enabled our Code Repository feature in order to both access the Daasity transformation code as well as modify a Script Manifest File enabling this code to execute

We recommend you review the Transformation Configuration section of our Help documentation to familiarize yourself with our workflow engine and script manifest files.

Script Manifest File (YML)

Upstream Transformation Dependencies

This code is dependent on the following integration being installed:

  • Tiktok

This code block is dependent on the following upstream code blocks being implemented:

Transformation Code Requirements

To enable the data transformation from the Tiktok schema into UMS, the following code must be run in a workflow:

  ums_tiktok:
    integrations:
      - tiktok
    scripts:
      - "github://platform-sql-shared/scripts/pro/1500_ums/1563_UMS_BAS_tiktok_spend.sql"
      - "github://platform-sql-shared/scripts/base/1500_ums/vendor_kpi/2563_UMS_BAS_tiktok.sql"

We recommend this code be added within a UMS block that transforms all your marketing integrations into the appropriate UMS tables

If you do not add the above code to a Script Manifest File and create a Workflow with the Script Manifest File added to the workflow, the data extracted from Tiktok will not be transformed into the downstream tables

UMS Master Spend

The following code will transform the specific Tiktok spend into the [ums.master_spend] table:

UMS Vendor Performance

The following code will transform the specific Tiktok data into the [ums.vendor_performance] table:

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