Documentation

The Join Data operator is a powerful addition to Self-Serve Data Transformation, designed to merge datasets seamlessly within your transformation workflows.

How Does It Work?

• Select Join Type
  Begin by choosing the type of join that suits your transformation needs:‍
• ~Left Join
• ~Right Join
• ~Outer Join
• ~Inner Join
  Each join type determines how the rows from your Primary Table and Secondary Table will be combined.

• Select the tables
  You can choose any table available in your database. Alternatively, you can select results from previous transformation steps as your Primary or Secondary table, enabling a dynamic, multi-step transformation workflow.

• Define Join Keys
  Join keys are the fields used to link the two tables. You will specify:
• ~Primary Table Keys: Keys from the main dataset.‍
• ~Secondary Table Keys: Keys from the table being joined.
  AI Autofill can assists in identifying the most appropriate keys. Simply click on the suggested keys, and the AI will automatically match fields between the two tables.

• Select Fields
  Specify the fields you want to include from both tables. This allows you to control the final output and ensure only the necessary data is carried forward.

• Update the model.
  Once the transformation is done, you can apply it to the model by clicking the «Run and Save» button.

Use Case

Joining Cross-Channel Data with Google Analytics

To illustrate, let’s walk through a real-world example:

• ‍Goal: Combine Cross-Channel data with Google Analytics metrics.‍
• Steps:

1. Select Left Join because we need all rows from Cross-Channel data and matching rows from Google Analytics.
2. Selects a tables:

• ~Set Primary Table to the result from the previous transformation (e.g., Cross-Channel Data).
• ~Set Secondary Table to the Google Analytics dataset.

3. Define Join Keys:

• ~Use the AI Autofill to identify matching keys:
• ~~account_id‍
• ~~campaign_name

4. Select fields:

• ~From Cross-Channel Data, include relevant fields from the previous transformation step.
• ~From Google Analytics, choose only the metrics you need (e.g., Sessions, Bounce Rate).

5. Click Run and Save to update the model.

The result is an enriched dataset where Google Analytics metrics are joined with Cross-Channel data, enabling deeper insights into campaign performance.

Conclusion

The Join Data operator provides a robust and flexible way to merge datasets, powered by AI for seamless key matching. Whether you’re preparing cross-channel analyses or enriching data with external sources, Join Data streamlines the process, allowing you to focus on generating actionable insights.