Google Ad Manager transfers

The BigQuery Data Transfer Service for Google Ad Manager allows you to automatically schedule and manage recurring load jobs for Google Ad Manager (formerly known as DoubleClick for Publishers) reporting data.

Supported reports

The BigQuery Data Transfer Service for Google Ad Manager currently supports the following reporting options:

• Data Transfer (Google Ad Manager DT) files
• Data Transfer fields
• Match Tables as provided via PQL
• Match Tables from CompanyService (v201908)
• Match Tables from OrderService (v201908)
• Match Tables from PlacementService (v201908)

For information on how Google Ad Manager reports are transformed into BigQuery tables and views, see Google Ad Manager report transformations.

Note: The BigQuery Data Transfer Service supports the following delimiters for Google Ad Manager DT files: Tab ( \t ), Pipe ( | ), Caret ( ^ ), and Comma ( , ). The Thorn (þ) delimiter is NOT currently supported.

Reporting option	Support
Supported API version	v201908
Schedule	Every 8 hours, based on the creation time. Not configurable
Refresh window	Last 2 days Not configurable
Maximum backfill duration	Last 60 days Google Ad Manager retains Data Transfer files for up to 60 days. Files older than 60 days are deleted by Google Ad Manager.

Before you begin

Before you create a Google Ad Manager transfer:

• Verify that you have completed all actions required to enable the BigQuery Data Transfer Service.
• Create a BigQuery dataset to store the Google Ad Manager data.

• Ensure that your organization has access to Google Ad Manager Data Transfer (Google Ad Manager DT) files. These files are delivered by the Google Ad Manager team to a Cloud Storage bucket. To gain access to Google Ad Manager DT files, complete the following steps. Additional charges from the Google Ad Manager team may apply.

  • Review the Overview of Data Transfer reports.
  • In the upper right corner of the page, click Contact Us.
  • Click Reports.
  • Email or chat with a representative and ask about access to Google Ad Manager Data Transfer files.

  After completing this step, you will receive a Cloud Storage bucket similar to the following:

  gdfp-12345678

  The Google Cloud team does NOT have the ability to generate or grant access to Google Ad Manager DT files on your behalf. Contact, Google Ad Manager support, for access to Google Ad Manager DT files.

• Enable API access to your Google Ad Manager network.

If you intend to create transfers using the classic BigQuery web UI, allow pop-ups from bigquery.cloud.google.com so you can view the permissions window. You must allow the BigQuery Data Transfer Service permission to manage your Google Ad Manager data for the transfer to succeed.

If you intend to setup transfer notifications, you must have pubsub.topics.setIamPolicy permissions for Pub/Sub. Pub/Sub permissions are not required if you just set up email notifications. For more information, see BigQuery Data Transfer Service run notifications.

Required permissions

• BigQuery: Ensure that the person creating the transfer has the following permissions in BigQuery:

  • bigquery.transfers.update permissions to create the transfer
  • bigquery.datasets.update permissions on the target dataset

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

• Google Ad Manager: Read access to the Google Ad Manager DT files stored in Cloud Storage. Permissions for Google Ad Manager DT files are managed by the Google Ad Manager team.

Setting up a Google Ad Manager transfer

Setting up a BigQuery data transfer for Google Ad Manager requires a:

• Cloud Storage bucket: The Cloud Storage bucket URI for your Google Ad Manager DT files as described in Before you begin. The bucket name should look like the following:

  gdfp-12345678

• Network Code: You'll find the Google Ad Manager network code in the URL when you are logged into your network. For example, in the URL https://admanager.google.com/2032576#delivery, 2032576 is your network code. For more information, see Get started with Google Ad Manager.

To create a BigQuery Data Transfer Service transfer for Google Ad Manager:

Console

1. Go to the BigQuery web UI.

   Go to the BigQuery web UI

2. Click Transfers.

3. Click Create Transfer.

4. On the Create Transfer page:

   • In the Source type section, for Source, choose Google Ad Manager.

     

   • In the Transfer config name section, for Display name, enter a name for the transfer such as My Transfer. The transfer name can be any value that allows you to easily identify the transfer if you need to modify it later.

     

   • In the Schedule options section, for Schedule, leave the default value (Start now) or click Start at a set time.

     • For Repeats, choose an option for how often to run the transfer.
       • Daily (default)
       • Weekly
       • Monthly
       • Custom
       • On-demand

     • For Start date and run time, enter the date and time to start the transfer. If you choose Start now, this option is disabled.

       

   • In the Destination settings section, for Destination dataset, choose the dataset you created to store your data.

     

   • In the Data source details section:

     • For Cloud Storage bucket, enter the name of the Cloud Storage bucket that stores your Data Transfer files. When you enter the bucket name, do not include gs://.

     • For Network code, enter your network code.

       

   • (Optional) In the Notification options section:

     • Click the toggle to enable email notifications. When you enable this option, the transfer administrator receives an email notification when a transfer run fails.
     • For Select a Pub/Sub topic, choose your topic name or click Create a topic. This option configures Pub/Sub run notifications for your transfer.

5. Click Save.

Classic UI

1. Go to the BigQuery web UI.

   Go to the BigQuery web UI

2. Click Transfers.

3. Click Add Transfer.

4. On the New Transfer page:

   • For Source, choose Google Ad Manager (formerly DFP).
   • For Display name, enter a name for the transfer such as My Transfer. The display name can be any value that allows you to easily identify it if you need to modify it later.
   • For Destination dataset, choose the appropriate dataset.
   • For Cloud Storage bucket, enter the name of the Cloud Storage bucket that stores your Data Transfer files. When you enter the bucket name, do not include gs://.

   • For Network code, enter your network code.

     

   • (Optional) Expand the Advanced section and configure run notifications for your transfer.

     • For Pub/Sub topic, enter your topic name, for example, projects/myproject/topics/mytopic.
     • Check Send email notifications to allow email notifications of transfer run failures.

     • Don't check Disabled when setting up a transfer. See Working with transfers to disable existing transfers.

       

5. Click Add.

6. When prompted, click Allow to give the BigQuery Data Transfer Service permission to access your Google Ad Manager reporting data and to manage the data in BigQuery. You must allow pop-ups from bigquery.cloud.google.com to view the permissions window.

   

CLI

Enter the bq mk command and supply the transfer creation flag — --transfer_config. The following flags are also required:

• --data_source
• --target_dataset
• --display_name
• --params

bq mk --transfer_config \
--project_id=project_id \
--target_dataset=dataset \
--display_name=name \
--params='parameters' \
--data_source=data_source

Where:

• project_id is your project ID.
• dataset is the target dataset for the transfer configuration.
• name is the display name for the transfer configuration. The transfer name can be any value that allows you to easily identify the transfer if you need to modify it later.
• parameters contains the parameters for the created transfer configuration in JSON format. For example: --params='{"param":"param_value"}'. For Google Ad Manager, you must supply the bucket and network_code, parameters. bucket is the Cloud Storage bucket that contains your Google Ad Manager DT files. network_code is your network code.
• data_source is the data source — dfp_dt (Google Ad Manager).

Note: Currently, you cannot configure notifications using the command-line tool.

You can also supply the --project_id flag to specify a particular project. If --project_id isn't specified, the default project is used.

For example, the following command creates a Google Ad Manager transfer named My Transfer using network code 12345678, Cloud Storage bucket gdfp-12345678, and target dataset mydataset. The transfer is created in the default project:

bq mk --transfer_config \
--target_dataset=mydataset \
--display_name='My Transfer' \
--params='{"bucket": "gdfp-12345678","network_code": "12345678"}' \
--data_source=dfp_dt

After running the command, you receive a message like the following:

[URL omitted] Please copy and paste the above URL into your web browser and follow the instructions to retrieve an authentication code.

Follow the instructions and paste the authentication code on the command line.

Note: When you create a Google Ad Manager transfer using the command-line tool, the transfer configuration is set up using the default value for Schedule (every 8 hours).

API

Use the projects.locations.transferConfigs.create method and supply an instance of the TransferConfig resource.

Caution: If you change the schema of a report, all files on that day must have the same schema, or the transfer for the entire day will fail.

Troubleshooting Google Ad Manager transfer setup

If you are having issues setting up your transfer, see Google Ad Manager transfer issues in Troubleshooting BigQuery Data Transfer Service transfer setup.

Querying your data

When your data is transferred to BigQuery, 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 pseudo-column in your query. For more information, see Querying partitioned tables.

Google Ad Manager sample queries

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

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

In each of the following queries, replace variables like dataset with your values. For example, replace network_code with your Google Ad Manager network code.

Impressions and unique users by city

The following sample query analyzes the number of impressions and unique users by city over the past 30 days.

Console

# START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
# END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
SELECT
  City,
  _DATA_DATE AS Date,
  count(*) AS imps,
  count(distinct UserId) AS uniq_users
FROM `dataset.NetworkImpressionsnetwork_code`
WHERE
  _DATA_DATE BETWEEN start_date AND end_date
GROUP BY City, Date

CLI

# START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
# END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
bq query --use_legacy_sql=false \
'SELECT
  City,
  _DATA_DATE AS Date,
  count(*) AS imps,
  count(distinct UserId) AS uniq_users
FROM `dataset.NetworkImpressions_network_code`
WHERE
  _DATA_DATE BETWEEN start_date AND end_date
GROUP BY City, Date'

Impressions and unique users by line item type

The following sample query analyzes the number of impressions and unique users by line item type over the past 30 days.

Console

# START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
# END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
SELECT
  MT.LineItemType AS LineItemType,
  DT._DATA_DATE AS Date,
  count(*) AS imps,
  count(distinct UserId) AS uniq_users
FROM `dataset.NetworkImpressionsnetwork_code` AS DT
LEFT JOIN `dataset.MatchTableLineItem_network_code` AS MT
ON
  DT.LineItemId = MT.Id
WHERE
  DT._DATA_DATE BETWEEN start_date AND end_date
GROUP BY LineItemType, Date
ORDER BY Date desc, imps desc

CLI

    # START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
    # END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
    bq query --use_legacy_sql=false \
    'SELECT
      MT.LineItemType AS LineItemType,
      DT._DATA_DATE AS Date,
      count(*) AS imps,
      count(distinct UserId) AS uniq_users
    FROM `dataset.NetworkImpressions_network_code` AS DT
    LEFT JOIN `dataset.MatchTableLineItem_network_code` AS MT
    ON
      DT.LineItemId = MT.Id
    WHERE
      DT._DATA_DATE BETWEEN start_date AND end_date
    GROUP BY LineItemType, Date
    ORDER BY Date desc, imps desc'

Impressions by ad unit

The following sample query analyzes the number of impressions by ad unit over the past 30 days.

Console

# START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
# END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
SELECT
  MT.AdUnitCode AS AdUnitCode,
  DT.DATA_DATE AS Date,
  count(*) AS imps
FROM `dataset.NetworkImpressionsnetwork_code` AS DT
LEFT JOIN `dataset.MatchTableLineItem_network_code` AS MT
ON
  DT.AdUnitId = MT.Id
WHERE
  DT._DATA_DATE BETWEEN start_date AND end_date
GROUP BY AdUnitCode, Date
ORDER BY Date desc, imps desc

CLI

# START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
# END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
bq query --use_legacy_sql=false \
'SELECT
  MT.AdUnitCode AS AdUnitCode,
  DT._DATA_DATE AS Date,
  count(*) AS imps
FROM `dataset.NetworkImpressions_network_code` AS DT
LEFT JOIN `dataset.MatchTableLineItem_network_code` AS MT
ON
  DT.AdUnitId = MT.Id
WHERE
  DT._DATA_DATE BETWEEN start_date AND end_date
GROUP BY AdUnitCode, Date
ORDER BY Date desc, imps desc'

Impressions by line item

The following sample query analyzes the number of impressions by line item over the past 30 days.

Console

# START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
# END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
SELECT
  MT.Name AS LineItemName,
  DT._DATA_DATE AS Date,
  count(*) AS imps
FROM `dataset.NetworkImpressionsnetwork_code` AS DT
LEFT JOIN `dataset.MatchTableLineItem_network_code` AS MT
ON
  DT.LineItemId = MT.Id
WHERE
  DT._DATA_DATE BETWEEN start_date AND end_date
GROUP BY LineItemName, Date
ORDER BY Date desc, imps desc

CLI

# START_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -31 DAY)
# END_DATE = DATE_ADD(CURRENT_DATE(), INTERVAL -1 DAY)
bq query --use_legacy_sql=false \
'SELECT
  MT.Name AS LineItemName,
  DT._DATA_DATE AS Date,
  count(*) AS imps
FROM `dataset.NetworkImpressions_network_code` AS DT
LEFT JOIN `dataset.MatchTableLineItem_network_code` AS MT
ON
  DT.LineItemId = MT.Id
WHERE
  DT._DATA_DATE BETWEEN start_date AND end_date
GROUP BY LineItemName, Date
ORDER BY Date desc, imps desc'

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上次更新日期：十二月 4, 2019