Introduction to BigQuery Omni

With BigQuery Omni, you can run BigQuery analytics on data stored in Amazon Simple Storage Service (Amazon S3) or Azure Blob Storage using BigLake tables.

Many organizations store data in multiple public clouds. Often, this data ends up being siloed, because it's hard to get insights across all of the data. You want to be able to analyze the data with a multi-cloud data tool that is inexpensive, fast, and does not create additional overhead of decentralized data governance. By using BigQuery Omni, we reduce these frictions with a unified interface.

To run BigQuery analytics on your external data, you first need to connect to Amazon S3 or Blob Storage. If you want to query external data, you would need to create a BigLake table that references Amazon S3 or Blob Storage data.

You can also move data between clouds to combine data across clouds using cross-cloud transfer. BigQuery Omni offers a cross-cloud analytics solution with the ability to analyze data where it is and the flexibility to replicate data when necessary. For more information, see Load data with cross-cloud transfer.

Architecture

BigQuery's architecture separates compute from storage, allowing BigQuery to scale out as needed to handle very large workloads. BigQuery Omni extends this architecture by running the BigQuery query engine in other clouds. As a result, you don't have to physically move data into BigQuery storage. Processing happens where that data already sits.

BigQuery Omni architecture

Query results can be returned to Google Cloud over a secure connection — for example, to be displayed in the Google Cloud console. Alternatively, you can write the results directly to Amazon S3 buckets or Blob Storage. In that case, there is no cross-cloud movement of the query results.

BigQuery Omni uses standard AWS IAM roles or Azure Active Directory principals to access the data in your subscription. You delegate read or write access to BigQuery Omni, and you can revoke access at any time.

Data flow when querying data

The following image describes how the data moves between Google Cloud and AWS or Azure for the following queries:

  • SELECT statement
  • CREATE EXTERNAL TABLE statement
Data movement between Google Cloud and AWS or Azure for queries.
Figure 1: Data movement between Google Cloud and AWS or Azure for queries.
  1. BigQuery control plane receive query jobs from you through Google Cloud console, bq command-line tool, an API method, or a client library.
  2. BigQuery control plane sends query jobs for processing to BigQuery data plane on AWS or Azure.
  3. BigQuery data plane receives the query from the control plane through a VPN connection.
  4. BigQuery data plane reads table data from your Amazon S3 bucket or Blob Storage.
  5. BigQuery data plane runs the query job on table data. The processing of table data occurs in the specified AWS or Azure region.
  6. The query result is transmitted from data plane to the control plane through the VPN connection.
  7. The BigQuery control plane receives the query job results for display to you in response to the query job. This data is stored for up to 24 hours.
  8. The query result is returned to you.

For more information, see Query Amazon S3 data and Blob Storage data.

Data flow when exporting data

The following image describes how data moves between Google Cloud and AWS or Azure during an EXPORT DATA statement.

Data movement between Google Cloud and AWS or Azure for export queries.
Figure 2: Data movement between Google Cloud and AWS or Azure for export queries.
  1. BigQuery control plane receives export query jobs from you through Google Cloud console, bq command-line tool, an API method, or a client library. The query contains the destination path for the query result in your Amazon S3 bucket or Blob Storage.
  2. BigQuery control plane sends export query jobs for processing to BigQuery data plane (on AWS or Azure).
  3. BigQuery data plane receives the export query from the control plane through the VPN connection.
  4. BigQuery data plane reads table data from your Amazon S3 bucket or Blob Storage.
  5. BigQuery data plane runs the query job on table data. Processing of table data occurs in the specified AWS or Azure region.
  6. BigQuery writes the query result to the specified destination path in your Amazon S3 bucket or Blob Storage.

For more information, see Export query results to Amazon S3 and Blob Storage.

Benefits

Performance. You can get insights faster, because data is not copied across clouds, and queries run in the same region where your data resides.

Cost. You save on network egress costs because the data doesn't move. There are no additional charges to your AWS or Azure account related to BigQuery Omni analytics, because the queries run on clusters managed by Google. You are only billed for running the queries, using the BigQuery pricing model.

Security and data governance. You manage the data in your own AWS or Azure subscription. You don't need to move or copy the raw data out of your public cloud. All computation happens in the BigQuery multi-tenant service which runs within the same region as your data.

Serverless architecture. Like the rest of BigQuery, BigQuery Omni is a serverless offering. Google deploys and manages the clusters that run BigQuery Omni. You don't need to provision any resources or manage any clusters.

Ease of management. BigQuery Omni provides a unified management interface through Google Cloud. BigQuery Omni can use your existing Google Cloud account and BigQuery projects. You can write a GoogleSQL query in the Google Cloud console to query data in AWS or Azure, and see the results displayed in the Google Cloud console.

Cross-cloud transfer. You can load data into standard BigQuery tables from S3 buckets and Blob Storage. For more information, see Transfer Amazon S3 data and Blob Storage data to BigQuery.

Limitations

In addition to the limitations for BigLake tables, the following limitations apply to BigQuery Omni, which includes BigLake tables based on Amazon S3 and Blob Storage data:

  • Working with data in any of the BigQuery Omni regions is not supported by Standard and Enterprise Plus editions. For more information about editions, see Introduction to BigQuery editions.
  • The OBJECT_PRIVILEGES, STREAMING_TIMELINE_BY_*, TABLE_SNAPSHOTS, and PARTITIONS INFORMATION_SCHEMA views are not available for BigLake tables based on Amazon S3 and Blob Storage data.
  • Materialized views are not supported.
  • JavaScript UDFs are not supported.

  • The following SQL statements are not supported:

  • The following limitations apply on querying and reading destination temporary tables (preview):

    • Querying destination temporary tables with the SELECT statement is not supported.
    • Using the BigQuery Storage Read API to read data from destination temporary tables is not supported.
    • When using the ODBC driver, high-throughput reads (EnableHTAPI option) is not supported.

  • Scheduled queries are only supported through the API or CLI method. The destination table option is disabled for queries. Only EXPORT DATA queries are allowed.

  • BigQuery Storage API is not available in the BigQuery Omni regions.

  • If your query uses the ORDER BY clause and has a result size larger than 256 MB, then your query fails. To resolve this, either reduce the result size or remove the ORDER BY clause from the query. For more information about BigQuery Omni quotas, see Quotas and limits.

Pricing

For information about pricing and limited-time offers in BigQuery Omni, see BigQuery Omni pricing.

Quotas and limits

For information about BigQuery Omni quotas, see Quotas and limits.

If your query result is larger than 20 GiB, consider exporting the results to Amazon S3 or Blob Storage. To learn about quotas for BigQuery Connection API, see BigQuery Connection API.

Locations

BigQuery Omni processes queries in the same location as the dataset that contains the tables you're querying. After you create the dataset, the location cannot be changed. Your data resides within your AWS or Azure account. BigQuery Omni regions support Enterprise edition reservations and on-demand compute (analysis) pricing. For more information about editions, see Introduction to BigQuery editions.
Region description Region name Collocated BigQuery region
AWS
AWS - US East (N. Virginia) aws-us-east-1 us-east-4
AWS - Asia Pacific (Seoul) aws-ap-northeast-2 asia-northeast3
Azure
Azure - East US 2 azure-eastus2 us-east-4

What's next