Zero Copy Database Explorer

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Zero Copy Database Explorer lets you build audiences directly from a connected Snowflake or Google BigQuery database, either with the visual builder or with SQL, including natural language through Athena. This page covers how you can preview matching records, save the audience, and use it in campaigns and content without copying the data into ZMP.

Learn more about connection, table mapping, column, and group setup in Zero Copy Database Explorer Setup.

Please get in touch with your Zeta Representative to enable this functionality for your account(s).

Using a Connected Database in Segmentation

1. From the main menu on the left, navigate to Audiences > Database Explorer to segment based on the data available to you for a given connection.

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Please note that you cannot join multiple sources of data when segmenting with this option.

2. Select your fields for segmentation.

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Please note that when creating a segment, some fields may be unavailable if they are not part of a mapped table that has already been selected.

3. Once the criteria are established, review the Results Preview panel to validate the returned records. When you are satisfied with the audience definition, run counts for the segment. Remember that the count is in terms of rows, not people.

In some cases, the numbers may be the same, but it is important to learn that, depending on how the data is structured, we may be returning duplicate rows for the same user_id.

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Create Audiences with Natural Language

You can create audiences directly from connected databases by describing the desired audience in plain language.

1. Open Database Explorer and select the connected data source.

2. Launch Athena from the audience creation workspace.

  • Describe the audience criteria using natural language.

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  • Athena generates the corresponding SQL query.

  • Review and optionally edit the generated SQL.

  • Preview the returned records.

  • Save the audience for use in campaigns and journeys.

Athena-generated SQL remains fully editable.

Previewing Query Results

Database Explorer now includes a Results Preview panel that allows you to validate your audience before saving or activating a segment.

Results Preview helps you confirm that:

  • Your filters are returning the expected records.

  • The correct fields are being included in the audience.

  • The audience definition matches your intended targeting criteria.

This capability is especially useful when working with large datasets and performing final audience quality checks before activation.

Viewing Results

After selecting your audience criteria, click on Preview Results. The Results Preview panel displays a sample of matching records.

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  • Review the returned fields and values to verify that your segment is configured correctly.

  • The preview initially displays a sample of records. Click Show More to load additional results.

You can progressively expand the preview up to 100 rows for deeper validation and spot-checking.

Results Preview can be used to validate audiences generated through either manual SQL creation or Athena-assisted natural language requests. Reviewing sample records before saving an audience helps confirm that the generated logic matches the intended targeting criteria.

Using Connected Database in Campaigns & Content

  • Once you have created a segment, you can select it as usual in your campaigns.

Note that if a User ID is not assigned, you will not be able to send a campaign.

  • Within content, you can access the fields marked for personalization by using the {{segment}} tag. You will get an object of the available fields, for example,

    {
  "users_id"=>"1518", 
  "users_fname"=>"Zeta", 
  "users_city"=>"New York", 
  "users_state"=>"New York",  
  "users_account_id"=>"A9036205", 
  "users_user_created_on_date"=>"2021-09-06 00:00:00", 
  "users_glbl_user_opt_in"=>"1", 
  "users_glbl_user_opt_out_dt"=>"", 
  "users_user_create_src"=>"site", 
  "uid"=>"zeta@zetaglobal.com"
  }

  • Within the {{segment}} tag, fields will be available with their alias names or original names, if not aliased. Table/fields will be delimited with an underscore; for example, users.id in segmentation will be available as {{segment.users_id}} in content.