Export Cloud Billing data to BigQuery

Cloud Billing export to BigQuery lets you export detailed Google Cloud billing data (such as usage, cost estimates, and pricing data) automatically throughout the day to a BigQuery dataset that you specify. Then you can access your Cloud Billing data from BigQuery for detailed analysis, or use a tool like Looker Studio to visualize your data. You can also use this export method to export data to a JSON file.

Timing is important. To access a more comprehensive set of Google Cloud billing data for your analysis needs, we recommend that you enable Cloud Billing data export to BigQuery at the same time that you create a Cloud Billing account.

See the limitations that might impact exporting your billing data to BigQuery.

Next steps

Managing and reporting costs effectively is a critical part of financial stewardship, whether you're running a multi-billion-dollar enterprise business or small household budget. Making data-driven decisions about your Google Cloud costs and usage starts with collecting the data you'll need to inform those decisions.

Refer to the guides in this section to learn about the following tasks:

Set up Cloud Billing data export to BigQuery

To start collecting your Cloud Billing data, you must enable Cloud Billing data export to BigQuery.

The setup guide provides best practice recommendations and detailed instructions for enabling Cloud Billing data export to BigQuery. These are the following types of Cloud Billing data you can enable for export:

  • Standard usage cost data - Contains standard Cloud Billing account cost usage information, such as account ID, invoice date, services, SKUs, projects, labels, locations, cost, usage, credits, adjustments, and currency.

    Use the Standard usage export to analyze broad trends in your cost data.

  • Detailed usage cost data - Contains detailed Cloud Billing account cost usage information. Includes everything in the standard usage cost data plus resource-level cost data, like a virtual machine or SSD that generates service usage.

    Use the Detailed usage export to analyze costs at the resource level, and identify specific resources that might be driving up costs. The detailed export includes resource-level information for the following products:

    • Compute Engine
    • Google Kubernetes Engine (GKE)
    • Cloud Run functions
    • Cloud Run

    To view information about GKE, enable cost allocation in detailed exports.

    Review the schema of the detailed usage cost data for further recommendations and limitations.

  • (Resellers only) Rebilling data export - Contains detailed Cloud Billing account cost usage information across all your Reseller Billing Accounts annotated with Partner specific attributes.

    Use the Rebilling data export to manage billing operations for your Google Cloud customers. Learn more about Repricing configurations which let you generate end-customer costs.

  • Pricing data - Contains Cloud Billing account pricing information, such as account ID, services, SKUs, products, geographic metadata, pricing units, currency, aggregation, and tiers.

    You can also get your Cloud Billing account pricing data in these ways:

  • Committed use discounts (CUD) metadata (Preview) - For customers who purchase CUDs, this daily export provides CUD metadata to a BigQuery table, which you can join with other billing data exports for better CUD management and reporting. CUD metadata includes information such as billing account ID, product ID, consumption model ID, commitment amount units and values, and more.

Using BigQuery to store and query Cloud Billing data will incur minimal fees. For more information, see Cost of use.

See the limitations that might impact exporting your billing data to BigQuery.

Understand the Cloud Billing data tables

After you enable Cloud Billing export to BigQuery, Cloud Billing data tables are automatically created in the BigQuery dataset.

To understand the data schema of your exported content, see the reference information for the contents of the Cloud Billing data that's exported to each table in the BigQuery dataset.

Find example queries for Cloud Billing data

For tips and guidance for using SQL to run queries on your Cloud Billing data, view the example queries.

On the example queries page, you'll find various SQL examples, including the following:

Cost of use

Using BigQuery to store and analyze billing usage and cost data typically incurs minimal fees.

  • Loading data into the designated dataset is free; this action takes advantage of BigQuery's pool of shared resources to load data in batches.
  • When exporting and analyzing Cloud Billing data with BigQuery, the associated cost depends on the amount of data you stream, store, and query.
  • Many partitioned table operations are free, including loading data into partitions, copying partitions, and exporting data from partitions. Though free, these operations are subject to BigQuery's quotas and limits.

Generally, querying the Detailed cost export might cost more than querying the Standard export. To optimize your costs, we recommend using the Standard export to analyze trends in your costs, and using the Detailed export to track costs at the resource level, and identify specific resources that might be driving your costs.

To get an idea of what charges to expect, see Estimating storage and query costs.

For more information on best practices for optimizing costs in BigQuery, see Control costs in BigQuery.

For detailed prices, review BigQuery pricing.

Limitations

Exporting Cloud Billing data to BigQuery is subject to the following limitations.

  • The table schema for any of the Cloud Billing data exports can change

    When the table schema changes, such as when new fields are added to a BigQuery table schema for a Cloud Billing data export, any queries that directly reference the exported columns might fail. To resolve this, we recommend creating BigQuery views that query the exported tables and present the information in your preferred structure.

    You can then adjust the queries that feed your reports and dashboards to pull from the views, instead of the exported tables. By using views, you can standardize the structure of the data used in your queries and dashboards.

    The views you create should normalize the data so that all of the relevant tables present the same schema to your queries. This protects you from future schema changes, allowing you to modify the view's underlying query in those instances when the data schema changes.

  • BigQuery dataset locations supported for use with Cloud Billing data

    BigQuery datasets are configured to use a location – either a multi-region location (EU or US), or a region location. The dataset location is set at creation time. After a dataset is created, its location can't be changed.

    Cloud Billing data export supports all multi-region locations (EU or US), but only a subset of region locations. When you're configuring your Cloud Billing export settings, if you create or select a dataset that's configured to use an unsupported region location, when you attempt to save your export settings, you'll see an Invalid dataset region error.

    The following table lists the multi-region locations and the region locations that are supported for use with BigQuery datasets that contain Cloud Billing data.

    Americas Asia Pacific Europe

    Multi-region: US

    Regions:

    • northamerica-northeast1 (Montréal)
    • southamerica-east1 (São Paulo)
    • us-central1 (Iowa)
    • us-east1 (South Carolina)
    • us-east4 (Northern Virginia)
    • us-west1 (Oregon)
    • us-west2 (Los Angeles)
    • us-west3 (Salt Lake City)
    • us-west4 (Las Vegas)

    Regions:

    • asia-east1 (Taiwan)
    • asia-east2 (Hong Kong)
    • asia-northeast1 (Tokyo)
    • asia-northeast2 (Osaka)
    • asia-northeast3 (Seoul)
    • asia-south1 (Mumbai)
    • asia-southeast1 (Singapore)
    • asia-southeast2 (Jakarta)
    • australia-southeast1 (Sydney)

    Multi-region: EU

    Regions:

    • europe-central2 (Warsaw)
    • europe-north1 (Finland)
    • europe-west1 (Belgium)
    • europe-west2 (London)
    • europe-west3 (Frankfurt)
    • europe-west4 (Netherlands)
    • europe-west6 (Zurich)

  • If you edit your export settings to update the project or dataset where your exported billing data is stored, previously exported billing data isn't backfilled to your new dataset. To include the billing information that was exported prior to the switch, you must manually join the new dataset with the previous dataset. For more information, see Join operation.

  • For your BigQuery datasets containing standard usage cost data or detailed usage cost data, the type of location you configure on the dataset impacts the timing of when your Google Cloud billing data is exported to the dataset:

    • If you configure the dataset to use a multi-region location (EU or US), the dataset includes Google Cloud billing data incurred from the start of the previous month from when you first enabled the export. That is, Google Cloud billing data is added retroactively for the current and previous month. For the initial backfill of exported data, it might take up to five days for your retroactive Cloud Billing data to finish exporting before you start seeing your current usage data. This retroactive backfill of data only occurs the first time you enable export. If you edit the export settings to change the project or dataset where your exported billing data is stored, or you are re-enabling the export, then billing data is not added retroactively.

    • If your dataset is configured to use a supported region location, your standard usage cost data and your detailed usage cost data only reflect Google Cloud billing data incurred starting from the date you enabled Cloud Billing export, and after. That is, Google Cloud billing data is not added retroactively for non-multi-region dataset locations, so you won't see Cloud Billing data from before you enable export.

    • For more details, see Data availability.

  • Your BigQuery datasets containing pricing data only collect Google Cloud billing data incurred from the date you set up Cloud Billing export, and after. That is, _Google Cloud pricing data isn't added retroactively, so you won't see Cloud Billing pricing data from before you enable export. For more details, see Data availability.

  • When exporting detailed usage cost data, the detailed export automatically includes resource-level information about Compute Engine. To view a breakdown of Google Kubernetes Engine (GKE) cluster costs in a detailed data export, you must also enable cost allocation for GKE.

  • Dataset encryption: Customer-managed encryption keys (CMEK) aren't supported when exporting billing data to BigQuery. If you enable CMEK encryption for your billing data dataset, this type of encryption prevents Cloud Billing from writing billing data to the appropriate tables within that dataset. Instead, you need to enable the dataset to use a Google-owned and Google-managed encryption key.

  • If you want to use BigQuery row-level security on the table that contains your exported data, you must give the Cloud Billing export service account billing-export-bigquery@system.gserviceaccount.com full access to the table using the BigQuery TRUE filter. The following command grants access to the Cloud Billing service account:

    CREATE ROW ACCESS POLICY cloud_billing_export_policy
ON `__project_id__.__dataset_id__.__table_id__`
GRANT TO ('serviceAccount:billing-export-bigquery@system.gserviceaccount.com')
FILTER USING (TRUE);

  • Resource-level Tags might take up to an hour to propagate to BigQuery exports. If a tag was added or removed within an hour, or if a resource has existed for less than an hour, it might not appear in the export.

    Resource-level tags are available for the following resources:

    • Compute Engine instances
    • Spanner instances
    • Cloud Run services
    • Artifact Registry repositories

  • If you use VPC Service Controls, your BigQuery exports might be blocked. To resolve, you need to manually exempt VPC.

Next
Set up billing data export to BigQuery