Query requirements for setting up a data pipeline to Google BigQuery

For the pipeline to work correctly, the schema of the data you get as a query result must correspond to the data schema of the dataset used for data upload (read more about the types of datasets).

Importing user audience data to Google Analytics

To import your users data to a Google Analytics remarketing audience, you need to create an "About user" data set.

When creating the data set, its data schema must contain:

Key: a Custom Dimension into which you collect ClientID;

Imported Data: a Custom Dimension into which you collect a value used to segment your audience.


You must have these Custom Dimensions created before you create the data set.

To import data from Google BigQuery, write a query that will get a user's Client ID and the condition, according to which the user will be assigned to a certain audience.

The query must look like this:

clientID as dimension{custom dimension index},
{user condition} as dimension{custom dimension index}

Once the pipeline is activated, you can set up a user audience in Google Analytics based on the data imported from Google BIgQuery.

Importing "About product" data to Google Analytics

In the dataset schema for the "About product" data:

  • the productSku field is required
  • additionally, you can specify any variations of the following fields: productBrand, productVariant, productCategoryHierarchy, productName, productPrice

Data schema requires to specify keys and imported dimensions and metrics:

ga:productSku ga:dimension23 ga:dimension24
12345 Red S


All Google Analytics dimensions and metrics have to be written without the ga: prefix in a query:

Google Analytics Google BigQuery query
ga:sourceMedium sourceMedium
ga:productSku productSku
ga:userId userId
ga:metric1 metric1
ga:... ...


To import data from Google BigQuery, your query should look as follows:

SELECT productSKU,dimension23,dimension24
FROM ...

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