Google Ads transfers

The BigQuery Data Transfer Service for Google Ads connector (formerly known as Google AdWords) lets you automatically schedule and manage recurring load jobs for Google Ads reporting data. To learn about recent data source changes, see BigQuery Data Transfer Service data source change log.

Supported reports

The BigQuery Data Transfer Service for Google Ads supports Google Ads API v16:

• Google Ads v16 reports

For information about how Google Ads reports are transformed into BigQuery Data Transfer Service tables and views, see Google Ads report transformations.

To map Google Ads reports to what you see in the Google Ads UI, see Mapping reports to the Google Ads UI.

Data ingestion from Google Ads transfers

Refresh windows

A refresh window is the number of days that a data transfer retrieves data when a data transfer occurs. For example, if the refresh window is three days and a daily transfer occurs, the BigQuery Data Transfer Service retrieves all data from your source table from the past three days. In this example, when a daily transfer occurs, the BigQuery Data Transfer Service creates a new BigQuery destination table partition with a copy of your source table data from the current day, then automatically triggers backfill runs to update the BigQuery destination table partitions with your source table data from the past two days. The automatically triggered backfill runs will either overwrite or incrementally update your BigQuery destination table, depending on whether or not incremental updates are supported in the BigQuery Data Transfer Service connector.

When you run a data transfer for the first time, the data transfer retrieves all source data available within the refresh window. For example, if the refresh window is three days and you run the data transfer for the first time, the BigQuery Data Transfer Service retrieves all source data within three days.

Refresh windows are mapped to the TransferConfig.data_refresh_window_days API field.

To retrieve data outside the refresh window, such as historical data, or to recover data from any transfer outages or gaps, you can initiate or schedule a backfill run.

Limitations

• The maximum frequency that you can configure a Google Ads data transfer for is once every 24 hours. By default, a transfer starts at the time that you create the transfer. However, you can configure the transfer start time when you create your transfer.
• The BigQuery Data Transfer Service does not support incremental data transfers during a Google Ads transfer. When you specify a date for a data transfer, all of the data that is available for that date is transferred.

Before you begin

Before you create a Google Ads data transfer, do the following:

• Verify that you have completed all actions required to enable the BigQuery Data Transfer Service.
• Create a BigQuery Data Transfer Service dataset to store the Google Ads data.
• If you intend to set up transfer run notifications for Pub/Sub, ensure that you have the pubsub.topics.setIamPolicy permission. Pub/Sub permissions are not required if you set up email notifications. For more information, see BigQuery Data Transfer Service run notifications.

Required permissions

Ensure that the person creating the data transfer has the following required permissions:

• BigQuery Data Transfer Service:

  • bigquery.transfers.update permissions to create the data transfer
  • Both bigquery.datasets.get and bigquery.datasets.update permissions on the target dataset

  The bigquery.admin predefined IAM role includes bigquery.transfers.update, bigquery.datasets.update and bigquery.datasets.get permissions. For more information about IAM roles in BigQuery Data Transfer Service, see Access control reference.

• Google Ads: Read access to the Google Ads Customer ID or manager account (MCC) that is used in the transfer configuration.

Create Google Ads data transfer

To create a data transfer for Google Ads reporting, you need either your Google Ads customer ID or your manager account (MCC). For information about retrieving your Google Ads customer ID, see Find your Customer ID.

To create a data transfer for Google Ads reporting, select one of the following options:

bq mk \
--transfer_config \
--project_id=PROJECT_ID \
--target_dataset=DATASET \
--display_name=NAME \
--params='PARAMETERS' \
--data_source=DATA_SOURCE \
--table_filter=TABLES \
--schedule=SCHEDULE
--refresh_window_days=REFRESH_DAYS
--service_account_name=SERVICE_ACCOUNT_NAME

bq mk \
--transfer_config \
--target_dataset=mydataset \
--display_name='My Transfer' \
--params='{"customer_id":"123-123-1234","exclude_removed_items":"true"}' \
--data_source=google_ads

import com.google.api.gax.rpc.ApiException;
import com.google.cloud.bigquery.datatransfer.v1.CreateTransferConfigRequest;
import com.google.cloud.bigquery.datatransfer.v1.DataTransferServiceClient;
import com.google.cloud.bigquery.datatransfer.v1.ProjectName;
import com.google.cloud.bigquery.datatransfer.v1.TransferConfig;
import com.google.protobuf.Struct;
import com.google.protobuf.Value;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

// Sample to create ads(formerly AdWords) transfer config
public class CreateAdsTransfer {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    final String projectId = "MY_PROJECT_ID";
    String datasetId = "MY_DATASET_ID";
    // the customer_id only allows digits and hyphen ('-').
    String customerId = "012-345-6789";
    String refreshWindow = "100";
    Map<String, Value> params = new HashMap<>();
    params.put("customer_id", Value.newBuilder().setStringValue(customerId).build());
    params.put("refreshWindow", Value.newBuilder().setStringValue(refreshWindow).build());
    TransferConfig transferConfig =
        TransferConfig.newBuilder()
            .setDestinationDatasetId(datasetId)
            .setDisplayName("Your Ads Transfer Config Name")
            .setDataSourceId("adwords")
            .setParams(Struct.newBuilder().putAllFields(params).build())
            .build();
    createAdsTransfer(projectId, transferConfig);
  }

  public static void createAdsTransfer(String projectId, TransferConfig transferConfig)
      throws IOException {
    try (DataTransferServiceClient client = DataTransferServiceClient.create()) {
      ProjectName parent = ProjectName.of(projectId);
      CreateTransferConfigRequest request =
          CreateTransferConfigRequest.newBuilder()
              .setParent(parent.toString())
              .setTransferConfig(transferConfig)
              .build();
      TransferConfig config = client.createTransferConfig(request);
      System.out.println("Ads transfer created successfully :" + config.getName());
    } catch (ApiException ex) {
      System.out.print("Ads transfer was not created." + ex.toString());
    }
  }
}

Manually trigger a Google Ads transfer

When you manually trigger a transfer for Google Ads, snapshots of Match Tables are taken once a day and stored in the partition for the latest run date. When you trigger a manual transfer, Match Table snapshots for the following tables are not updated:

• Ad
• AdGroup
• AdGroupAudience
• AdGroupBidModifier
• AdGroupAdLabel
• AdGroupCriterion
• AdGroupCriterionLabel
• AdGroupLabel
• AgeRange
• Asset
• AssetGroup
• AssetGroupAsset
• AssetGroupListingGroupFilter
• AssetGroupSignal
• Audience
• BidGoal
• Budget
• Campaign
• CampaignAudience
• CampaignCriterion
• CampaignLabel
• Customer
• Gender
• Keyword
• LocationBasedCampaignCriterion
• ParentalStatus
• Placement
• Video

PMax support

The Google Ads connector lets you export PMax campaigns data. You must select the Include PMax Campaign Tables checkbox when creating a data transfer, as PMax data is not exported by default.

Including PMax data removes ad_group fields from certain tables and includes new tables. You cannot include ad_group fields because the Google Ads API filters the PMax data.

The following tables exclude ad_group related columns when the Include PMax Campaign Tables checkbox is selected:

• GeoStats
• GeoConversionStats
• ShoppingProductConversionStats
• ShoppingProductStats
• LocationsUserLocationsStats

The following tables are added when the Include PMax Campaign Tables checkbox is selected:

• Asset
• AssetGroup
• AssetGroupAsset
• AssetGroupListingGroupFilter
• AssetGroupSignal
• Audience
• AssetGroupProductGroupStats
• CampaignAssetStats

Support for Google Ads manager accounts

Existing customers who have multiple Customer ID-specific Google Ads Transfers are encouraged to set up a single Google Ads Transfer at the Manager Account (MCC) level, schedule a backfill, and disable individual Customer ID-specific Google Ads Transfers.

Using Google Ads manager accounts provides several benefits over using individual Customer IDs:

• You no longer need to manage multiple data transfers to report on multiple Customer IDs.
• Cross-customer queries are much simpler to write because all the Customer IDs are stored in the same table.
• Using MCCs alleviates BigQuery Data Transfer Service load quota issues because multiple Customer IDs are loaded in the same job.

For more information about Google Ads manager accounts (MCCs), see Working with managed accounts and About linking accounts to your manager account.

Example

The following list shows the Customer IDs linked to particular Google Ads manager accounts:

• 1234567890 — root manager account
  • 1234 — sub-manager account
    • 1111 — Customer ID
    • 2222 — Customer ID
    • 3333 — Customer ID
    • 4444 — Customer ID
    • 567 — sub-manager account
      • 5555 — Customer ID
      • 6666 — Customer ID
      • 7777 — Customer ID
  • 89 — sub-manager account
    • 8888 — Customer ID
    • 9999 — Customer ID
  • 0000 — Customer ID

Each Customer ID linked to a manager account appears in each report. For more information about the Google Ads reporting structure in BigQuery Data Transfer Service, see Google Ads report transformation.

Transfer configuration for Customer ID 1234567890

A transfer configuration for the root manager account (Customer ID 1234567890) would generate data transfer runs that include the following Customer IDs:

• 1111 (via sub-manager account 1234)
• 2222 (via sub-manager account 1234)
• 3333 (via sub-manager account 1234)
• 4444 (via sub-manager account 1234)
• 5555 (via sub-manager account 567 and sub-manager account 1234)
• 6666 (via sub-manager account 567 and sub-manager account 1234)
• 7777 (via sub-manager account 567 and sub-manager account 1234)
• 8888 (via sub-manager account 89)
• 9999 (via sub-manager account 89)
• 0000 (individual Customer ID)

Transfer configuration for Customer ID 1234

A transfer configuration for sub-manager account 123 (Customer ID 1234) would generate data transfer runs that include the following Customer IDs:

• 1111
• 2222
• 3333
• 4444
• 5555 (via sub-manager account 567)
• 6666 (via sub-manager account 567)
• 7777 (via sub-manager account 567)

Transfer configuration for Customer ID 567

A transfer configuration for sub-manager account 567 (Customer ID 567) would generate data transfer runs that include the following Customer IDs:

• 5555
• 6666
• 7777

Transfer configuration for Customer ID 89

A transfer configuration for sub-manager account 89 (Customer ID 89) would generate data transfer runs that include the following Customer IDs:

• 8888
• 9999

Transfer configuration for Customer ID 0000

A transfer configuration for Customer ID 0000 would generate data transfer runs that include only the individual Customer ID:

• 0000

Migrate Google Ads data to MCCs

To migrate your existing Google Ads data in BigQuery Data Transfer Service to the MCC structure, you can set up a backfill to add your existing data to the tables created by the transfer configuration linked to the manager account. Note that when you schedule a backfill, match tables are not updated.

Troubleshoot Google Ads transfer setup

If you are having issues setting up your data transfer, see Google Ads transfer issues in Troubleshooting transfer configurations.

Query your data

When your data is transferred to BigQuery Data Transfer Service, the data is written to ingestion-time partitioned tables. For more information, see Introduction to partitioned tables.

If you query your tables directly instead of using the auto-generated views, you must use the _PARTITIONTIME pseudocolumn in your query. For more information, see Querying partitioned tables.

Google Ads sample queries

You can use the following Google Ads sample queries to analyze your transferred data. You can also use the queries in a visualization tool such as Looker Studio. These queries are provided to help you get started on querying your Google Ads data with BigQuery Data Transfer Service. For additional questions about what you can do with these reports, contact your Google Ads technical representative.

In each of the following queries, replace dataset with your dataset name. Replace customer_id with your Google Ads Customer ID.

If you query your tables directly instead of using the auto-generated views, you must use the _PARTITIONTIME pseudocolumn in your query. For more information, see Querying partitioned tables.

Campaign performance

The following sample query analyzes Google Ads campaign performance for the past 30 days.

SELECT
  c.customer_id,
  c.campaign_name,
  c.campaign_status,
  SUM(cs.metrics_impressions) AS Impressions,
  SUM(cs.metrics_interactions) AS Interactions,
  (SUM(cs.metrics_cost_micros) / 1000000) AS Cost
FROM
  `DATASET.ads_Campaign_CUSTOMER_ID` c
LEFT JOIN
  `DATASET.ads_CampaignBasicStats_CUSTOMER_ID` cs
ON
  (c.campaign_id = cs.campaign_id
  AND cs._DATA_DATE BETWEEN
  DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY) AND DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY))
WHERE
  c._DATA_DATE = c._LATEST_DATE
GROUP BY
  1, 2, 3
ORDER BY
  Impressions DESC

  bq query --use_legacy_sql=false '
  SELECT
    c.customer_id,
    c.campaign_name,
    c.campaign_status,
    SUM(cs.metrics_impressions) AS Impressions,
    SUM(cs.metrics_interactions) AS Interactions,
    (SUM(cs.metrics_cost_micros) / 1000000) AS Cost
  FROM
    `DATASET.ads_Campaign_CUSTOMER_ID` c
  LEFT JOIN
    `DATASET.ads_CampaignBasicStats_CUSTOMER_ID` cs
  ON
    (c.campaign_id = cs.campaign_id
    AND cs._DATA_DATE BETWEEN
    DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY) AND DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY))
  WHERE
    c._DATA_DATE = c._LATEST_DATE
  GROUP BY
    1, 2, 3
  ORDER BY
    Impressions DESC'

Count of keywords

The following sample query analyzes keywords by campaign, ad group, and keyword status. This query uses the KeywordMatchType function. Keyword match types help control which searches can trigger your ad. For more information about keyword matching options, see About keyword matching options.

  SELECT
    c.campaign_status AS CampaignStatus,
    a.ad_group_status AS AdGroupStatus,
    k.ad_group_criterion_status AS KeywordStatus,
    k.ad_group_criterion_keyword_match_type AS KeywordMatchType,
    COUNT(*) AS count
  FROM
    `DATASET.ads_Keyword_CUSTOMER_ID` k
    JOIN
    `DATASET.ads_Campaign_CUSTOMER_ID` c
  ON
    (k.campaign_id = c.campaign_id AND k._DATA_DATE = c._DATA_DATE)
  JOIN
    `DATASET.ads_AdGroup_CUSTOMER_ID` a
  ON
    (k.ad_group_id = a.ad_group_id AND k._DATA_DATE = a._DATA_DATE)
  WHERE
    k._DATA_DATE = k._LATEST_DATE
  GROUP BY
    1, 2, 3, 4

  bq query --use_legacy_sql=false '
  SELECT
    c.campaign_status AS CampaignStatus,
    a.ad_group_status AS AdGroupStatus,
    k.ad_group_criterion_status AS KeywordStatus,
    k.ad_group_criterion_keyword_match_type AS KeywordMatchType,
    COUNT(*) AS count
  FROM
    `DATASET.ads_Keyword_CUSTOMER_ID` k
  JOIN
    `DATASET.ads_Campaign_CUSTOMER_ID` c
  ON
    (k.campaign_id = c.campaign_id AND k._DATA_DATE = c._DATA_DATE)
  JOIN
    `DATASET.ads_AdGroup_CUSTOMER_ID` a
  ON
    (k.ad_group_id = a.ad_group_id AND k._DATA_DATE = a._DATA_DATE)
  WHERE
    k._DATA_DATE = k._LATEST_DATE
  GROUP BY
    1, 2, 3, 4'