Create Amazon S3 BigLake tables

This document describes how to create an Amazon Simple Storage Service (Amazon S3) BigLake table. A BigLake table lets you use access delegation to query data in Amazon S3. Access delegation decouples access to the BigLake table from access to the underlying datastore.

For information about how data flows between BigQuery and Amazon S3, see Data flow when querying data.

Before you begin

Ensure that you have a connection to access Amazon S3 data.

Required roles

To get the permissions that you need to create an external table, ask your administrator to grant you the BigQuery Admin (roles/bigquery.admin) IAM role on your dataset. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the permissions required to create an external table. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to create an external table:

bigquery.tables.create
bigquery.connections.delegate

You might also be able to get these permissions with custom roles or other predefined roles.

Create a dataset

Before you create an external table, you need to create a dataset in the supported region. Select one of the following options:

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.Dataset;
import com.google.cloud.bigquery.DatasetInfo;

// Sample to create a aws dataset
public class CreateDatasetAws {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "MY_PROJECT_ID";
    String datasetName = "MY_DATASET_NAME";
    // Note: As of now location only supports aws-us-east-1
    String location = "aws-us-east-1";
    createDatasetAws(projectId, datasetName, location);
  }

  public static void createDatasetAws(String projectId, String datasetName, String location) {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      DatasetInfo datasetInfo =
          DatasetInfo.newBuilder(projectId, datasetName).setLocation(location).build();

      Dataset dataset = bigquery.create(datasetInfo);
      System.out.println(
          "Aws dataset created successfully :" + dataset.getDatasetId().getDataset());
    } catch (BigQueryException e) {
      System.out.println("Aws dataset was not created. \n" + e.toString());
    }
  }
}

Create BigLake tables on unpartitioned data

Select one of the following options:

Console

Go to the BigQuery page.

Go to BigQuery
In the left pane, click Explorer:

If you don't see the left pane, click Expand left pane to open the pane.
In the Explorer pane, expand your project, click Datasets, and then select a dataset.
In the Dataset info section, click Create table.
On the Create table page, in the Source section, do the following:
1. For Create table from, select Amazon S3.
2. For Select S3 path, enter a URI pointing to the Amazon S3 data in the format s3://BUCKET_NAME/PATH. Replace BUCKET_NAME with the name of the Amazon S3 bucket; the bucket's region should be the same as the dataset's region. Replace PATH with the path that you would like to write the exported file to; it can contain one wildcard *.
3. For File format, select the data format in Amazon S3. Supported formats are AVRO, CSV, DELTA_LAKE, ICEBERG, JSONL, ORC, and PARQUET.
In the Destination section, specify the following details:
1. For Dataset, choose the appropriate dataset.
2. In the Table field, enter the name of the table.
3. Verify that Table type is set to External table.
4. For Connection ID, choose the appropriate connection ID from the drop-down. For information about connections, see Connect to Amazon S3.
In the Schema section, you can either enable schema auto-detection or manually specify a schema if you have a source file. If you don't have a source file, you must manually specify a schema.
- To enable schema auto-detection, select the Auto-detect option.
- To manually specify a schema, leave the Auto-detect option unchecked. Enable Edit as text and enter the table schema as a JSON array.
Click Create table.

SQL

To create a BigLake table, use the CREATE EXTERNAL TABLE statement with the WITH CONNECTION clause:

In the Google Cloud console, go to the BigQuery page.

Go to BigQuery
In the query editor, enter the following statement:
```
CREATE EXTERNAL TABLE DATASET_NAME.TABLE_NAME
  WITH CONNECTION `AWS_LOCATION.CONNECTION_NAME`
  OPTIONS (
    format = "DATA_FORMAT",
    uris = ["S3_URI"],
    max_staleness = STALENESS_INTERVAL,
    metadata_cache_mode = 'CACHE_MODE');
```
Replace the following:
- DATASET_NAME: the name of the dataset you created
- TABLE_NAME: the name you want to give to this table
- AWS_LOCATION: an AWS location in Google Cloud (for example, `aws-us-east-1`)
- CONNECTION_NAME: the name of the connection you created
- DATA_FORMAT: any of the supported BigQuery federated formats (such as AVRO, CSV, DELTA_LAKE, ICEBERG, or PARQUET (preview))
- S3_URI: a URI pointing to the Amazon S3 data (for example, s3://bucket/path)
- STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. For more information about metadata caching considerations, see Metadata caching for performance.
  To disable metadata caching, specify 0. This is the default.
  
  To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 4 HOUR for a 4 hour staleness interval. With this value, operations against the table use cached metadata if it has been refreshed within the past 4 hours. If the cached metadata is older than that, the operation retrieves metadata from Amazon S3 instead.
- CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually. For more information about metadata caching considerations, see Metadata caching for performance.
  Set to AUTOMATIC for the metadata cache to be refreshed at a system-defined interval, usually somewhere between 30 and 60 minutes.
  
  Set to MANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call the BQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.
  
  You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.
Click Run.

For more information about how to run queries, see Run an interactive query.

Example:

CREATE EXTERNAL TABLE awsdataset.awstable
  WITH CONNECTION `aws-us-east-1.s3-read-connection`
  OPTIONS (
    format="CSV",
    uris=["s3://s3-bucket/path/file.csv"],
    max_staleness = INTERVAL 1 DAY,
    metadata_cache_mode = 'AUTOMATIC'
);

bq

Create a table definition file:

bq mkdef  \
--source_format=DATA_FORMAT \
--connection_id=AWS_LOCATION.CONNECTION_NAME \
--metadata_cache_mode=CACHE_MODE \
S3_URI > table_def

Replace the following:

DATA_FORMAT: any of the supported BigQuery federated formats (such as AVRO, CSV, DELTA_LAKE, ICEBERG, or PARQUET).
S3_URI: a URI pointing to the Amazon S3 data (for example, s3://bucket/path).
AWS_LOCATION: an AWS location in Google Cloud (for example, aws-us-east-1).
CONNECTION_NAME: the name of the connection you created.
CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually. You only need to include this flag if you also plan to use the --max_staleness flag in the subsequent bq mk command to enable metadata caching. For more information about metadata caching considerations, see Metadata caching for performance.

Set to AUTOMATIC for the metadata cache to be refreshed at a system-defined interval, usually somewhere between 30 and 60 minutes.

Set to MANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call the BQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache. You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.

Next, create the BigLake table:

bq mk --max_staleness=STALENESS_INTERVAL --external_table_definition=table_def DATASET_NAME.TABLE_NAME

Replace the following:

STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. For more information about metadata caching considerations, see Metadata caching for performance.

To disable metadata caching, specify 0. This is the default.

To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 4 HOUR for a 4 hour staleness interval. With this value, operations against the table use cached metadata if it has been refreshed within the past 4 hours. If the cached metadata is older than that, the operation retrieves metadata from Amazon S3 instead.
DATASET_NAME: the name of the dataset you created.
TABLE_NAME: the name you want to give to this table.

For example, the following command creates a new BigLake table, awsdataset.awstable, which can query your Amazon S3 data that's stored at the path s3://s3-bucket/path/file.csv and has a read connection in the location aws-us-east-1:

bq mkdef  \
--autodetect \
--source_format=CSV \
--connection_id=aws-us-east-1.s3-read-connection \
--metadata_cache_mode=AUTOMATIC \
s3://s3-bucket/path/file.csv > table_def

bq mk --max_staleness=INTERVAL "1" HOUR \
--external_table_definition=table_def awsdataset.awstable

API

Call the tables.insert method API method, and create an ExternalDataConfiguration in the Table resource that you pass in.

Specify the schema property or set the autodetect property to true to enable schema auto detection for supported data sources.

Specify the connectionId property to identify the connection to use for connecting to Amazon S3.

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

import com.google.cloud.bigquery.BigQuery;
import com.google.cloud.bigquery.BigQueryException;
import com.google.cloud.bigquery.BigQueryOptions;
import com.google.cloud.bigquery.CsvOptions;
import com.google.cloud.bigquery.ExternalTableDefinition;
import com.google.cloud.bigquery.Field;
import com.google.cloud.bigquery.Schema;
import com.google.cloud.bigquery.StandardSQLTypeName;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.bigquery.TableInfo;

// Sample to create an external aws table
public class CreateExternalTableAws {

  public static void main(String[] args) {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "MY_PROJECT_ID";
    String datasetName = "MY_DATASET_NAME";
    String tableName = "MY_TABLE_NAME";
    String connectionId = "MY_CONNECTION_ID";
    String sourceUri = "s3://your-bucket-name/";
    CsvOptions options = CsvOptions.newBuilder().setSkipLeadingRows(1).build();
    Schema schema =
        Schema.of(
            Field.of("name", StandardSQLTypeName.STRING),
            Field.of("post_abbr", StandardSQLTypeName.STRING));
    ExternalTableDefinition externalTableDefinition =
        ExternalTableDefinition.newBuilder(sourceUri, options)
            .setConnectionId(connectionId)
            .setSchema(schema)
            .build();
    createExternalTableAws(projectId, datasetName, tableName, externalTableDefinition);
  }

  public static void createExternalTableAws(
      String projectId,
      String datasetName,
      String tableName,
      ExternalTableDefinition externalTableDefinition) {
    try {
      // Initialize client that will be used to send requests. This client only needs to be created
      // once, and can be reused for multiple requests.
      BigQuery bigquery = BigQueryOptions.getDefaultInstance().getService();

      TableId tableId = TableId.of(projectId, datasetName, tableName);
      TableInfo tableInfo = TableInfo.newBuilder(tableId, externalTableDefinition).build();

      bigquery.create(tableInfo);
      System.out.println("Aws external table created successfully");

      // Clean up
      bigquery.delete(TableId.of(projectId, datasetName, tableName));
    } catch (BigQueryException e) {
      System.out.println("Aws external was not created." + e.toString());
    }
  }
}

Create BigLake tables on partitioned data

You can create a BigLake table for Hive partitioned data in Amazon S3. After you create an externally partitioned table, you can't change the partition key. You need to recreate the table to change the partition key.

To create a BigLake table based on Hive partitioned data, select one of the following options:

Console

Go to the BigQuery page.

Go to BigQuery
In the left pane, click Explorer:
In the Explorer pane, expand your project, click Datasets, and select a dataset.
Click Create table. This opens the Create table pane.
In the Source section, specify the following details:
1. For Create table from, select Amazon S3.
2. Provide the path to the folder, using wildcards. For example, s3://mybucket/*.
  
  The folder must be in the same location as the dataset that contains the table you want to create, append, or overwrite.
3. From the File format list, select the file type.
4. Select the Source data partitioning checkbox, and then specify the following details:
  1. For Select Source URI Prefix, enter the URI prefix. For example, s3://mybucket/my_files.
  2. Optional: To require a partition filter on all queries for this table, select the Require partition filter checkbox. Requiring a partition filter can reduce cost and improve performance. For more information, see Requiring predicate filters on partition keys in queries.
  3. In the Partition inference mode section, select one of the following options:
    - Automatically infer types: set the partition schema detection mode to AUTO.
    - All columns are strings: set the partition schema detection mode to STRINGS.
    - Provide my own: set the partition schema detection mode to CUSTOM and manually enter the schema information for the partition keys. For more information, see Custom partition key schema.
In the Destination section, specify the following details:
1. For Project, select the project in which you want to create the table.
2. For Dataset, select the dataset in which you want to create the table.
3. For Table, enter the name of the table that you want to create.
4. For Table type, verify that External table is selected.
5. For Connection ID, select the connection that you created earlier.
In the Schema section, you can either enable schema auto-detection or manually specify a schema if you have a source file. If you don't have a source file, you must manually specify a schema.
- To enable schema auto-detection, select the Auto-detect option.
- To manually specify a schema, leave the Auto-detect option unchecked. Enable Edit as text and enter the table schema as a JSON array.
To ignore rows with extra column values that don't match the schema, expand the Advanced options section and select Unknown values.
Click Create table.

SQL

Use the CREATE EXTERNAL TABLE DDL statement:

In the Google Cloud console, go to the BigQuery page.

Go to BigQuery
In the query editor, enter the following statement:
```
CREATE EXTERNAL TABLE `PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`
WITH PARTITION COLUMNS
(
  PARTITION_COLUMN PARTITION_COLUMN_TYPE,
)
WITH CONNECTION `PROJECT_ID.REGION.CONNECTION_ID`
OPTIONS (
  hive_partition_uri_prefix = "HIVE_PARTITION_URI_PREFIX",
  uris=['FILE_PATH'],
  format ="TABLE_FORMAT"
  max_staleness = STALENESS_INTERVAL,
  metadata_cache_mode = 'CACHE_MODE'
);
```
Replace the following:
- PROJECT_ID: the name of your project in which you want to create the table—for example, myproject
- DATASET: the name of the BigQuery dataset that you want to create the table in—for example, mydataset
- EXTERNAL_TABLE_NAME: the name of the table that you want to create—for example, mytable
- PARTITION_COLUMN: the name of the partitioning column
- PARTITION_COLUMN_TYPE: the type of the partitioning column
- REGION: the region that contains the connection—for example, us
- CONNECTION_ID: the name of the connection—for example, myconnection
- HIVE_PARTITION_URI_PREFIX: hive partitioning uri prefix–for example: s3://mybucket/
- FILE_PATH: path to the data source for the external table that you want to create—for example: s3://mybucket/*.parquet
- TABLE_FORMAT: the format of the table that you want to create—for example, PARQUET
- STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. For more information about metadata caching considerations, see Metadata caching for performance.
  To disable metadata caching, specify 0. This is the default.
  
  To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 4 HOUR for a 4 hour staleness interval. With this value, operations against the table use cached metadata if it has been refreshed within the past 4 hours. If the cached metadata is older than that, the operation retrieves metadata from Amazon S3 instead.
- CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually. For more information about metadata caching considerations, see Metadata caching for performance.
  Set to AUTOMATIC for the metadata cache to be refreshed at a system-defined interval, usually somewhere between 30 and 60 minutes.
  
  Set to MANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call the BQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.
  
  You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.
Click Run.

For more information about how to run queries, see Run an interactive query.

Examples

The following example creates a BigLake table over partitioned data in Amazon S3. The schema is autodetected.

CREATE EXTERNAL TABLE `my_dataset.my_table`
WITH PARTITION COLUMNS
(
  sku STRING,
)
WITH CONNECTION `us.my-connection`
OPTIONS(
  hive_partition_uri_prefix = "s3://mybucket/products",
  uris = ['s3://mybucket/products/*']
  max_staleness = INTERVAL 1 DAY,
  metadata_cache_mode = 'AUTOMATIC'
);

bq

First, use the bq mkdef command to create a table definition file:

bq mkdef \
--source_format=SOURCE_FORMAT \
--connection_id=REGION.CONNECTION_ID \
--hive_partitioning_mode=PARTITIONING_MODE \
--hive_partitioning_source_uri_prefix=URI_SHARED_PREFIX \
--require_hive_partition_filter=BOOLEAN \
--metadata_cache_mode=CACHE_MODE \
 URIS > DEFINITION_FILE

Replace the following:

SOURCE_FORMAT: the format of the external data source. For example, CSV.
REGION: the region that contains the connection—for example, us.
CONNECTION_ID: the name of the connection—for example, myconnection.
PARTITIONING_MODE: the Hive partitioning mode. Use one of the following values:
- AUTO: Automatically detect the key names and types.
- STRINGS: Automatically convert the key names to strings.
- CUSTOM: Encode the key schema in the source URI prefix.
URI_SHARED_PREFIX: the source URI prefix.
BOOLEAN: specifies whether to require a predicate filter at query time. This flag is optional. The default value is false.
CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually. You only need to include this flag if you also plan to use the --max_staleness flag in the subsequent bq mk command to enable metadata caching. For more information about metadata caching considerations, see Metadata caching for performance.

Set to AUTOMATIC for the metadata cache to be refreshed at a system-defined interval, usually somewhere between 30 and 60 minutes.

Set to MANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call the BQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache. You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.
URIS: the path to the Amazon S3 folder, using wildcard format.
DEFINITION_FILE: the path to the table definition file on your local machine.

If PARTITIONING_MODE is CUSTOM, include the partition key schema in the source URI prefix, using the following format:

--hive_partitioning_source_uri_prefix=URI_SHARED_PREFIX/{KEY1:TYPE1}/{KEY2:TYPE2}/...

After you create the table definition file, use the bq mk command to create the BigLake table:

bq mk --max_staleness=STALENESS_INTERVAL \
--external_table_definition=DEFINITION_FILE \
DATASET_NAME.TABLE_NAME \
SCHEMA

Replace the following:

STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the BigLake table, and how fresh the cached metadata must be in order for the operation to use it. For more information about metadata caching considerations, see Metadata caching for performance.

To disable metadata caching, specify 0. This is the default.

To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 4 HOUR for a 4 hour staleness interval. With this value, operations against the table use cached metadata if it has been refreshed within the past 4 hours. If the cached metadata is older than that, the operation retrieves metadata from Amazon S3 instead.
DEFINITION_FILE: the path to the table definition file.
DATASET_NAME: the name of the dataset that contains the table.
TABLE_NAME: the name of the table you're creating.
SCHEMA: specifies a path to a JSON schema file, or specifies the schema in the form field:data_type,field:data_type,.... To use schema auto-detection, omit this argument.

Examples

The following example uses AUTO Hive partitioning mode for Amazon S3 data:

bq mkdef --source_format=CSV \
  --connection_id=us.my-connection \
  --hive_partitioning_mode=AUTO \
  --hive_partitioning_source_uri_prefix=s3://mybucket/myTable \
  --metadata_cache_mode=AUTOMATIC \
  s3://mybucket/* > mytable_def

bq mk --max_staleness=INTERVAL "1" HOUR \
  --external_table_definition=mytable_def \
  mydataset.mytable \
  Region:STRING,Quarter:STRING,Total_sales:INTEGER

The following example uses STRING Hive partitioning mode for Amazon S3 data:

bq mkdef --source_format=CSV \
  --connection_id=us.my-connection \
  --hive_partitioning_mode=STRING \
  --hive_partitioning_source_uri_prefix=s3://mybucket/myTable \
  --metadata_cache_mode=AUTOMATIC \
  s3://mybucket/myTable/* > mytable_def

bq mk --max_staleness=INTERVAL "1" HOUR \
  --external_table_definition=mytable_def \
  mydataset.mytable \
  Region:STRING,Quarter:STRING,Total_sales:INTEGER

API

To set Hive partitioning using the BigQuery API, include the hivePartitioningOptions object in the ExternalDataConfiguration object when you create the table definition file. To create a BigLake table, you must also specify a value for the connectionId field.

If you set the hivePartitioningOptions.mode field to CUSTOM, you must encode the partition key schema in the hivePartitioningOptions.sourceUriPrefix field as follows: s3://BUCKET/PATH_TO_TABLE/{KEY1:TYPE1}/{KEY2:TYPE2}/...

To enforce the use of a predicate filter at query time, set the hivePartitioningOptions.requirePartitionFilter field to true.

Delta Lake tables

Delta Lake is an open source table format that supports petabyte scale data tables. Delta Lake tables can be queried as both temporary and permanent tables, and is supported as a BigLake table.

Schema synchronization

Delta Lake maintains a canonical schema as part of its metadata. You can't update a schema using a JSON metadata file. To update the schema:

Use the bq update command with the --autodetect_schema flag:
```
bq update --autodetect_schema
PROJECT_ID:DATASET.TABLE
```
Replace the following:
- PROJECT_ID: the project ID containing the table that you want to update
- DATASET: the dataset containing the table that you want to update
- TABLE: the table that you want to update

Type conversion

BigQuery converts Delta Lake data types to the following BigQuery data types:

Delta Lake Type	BigQuery Type
`boolean`	`BOOL`
`byte`	`INT64`
`int`	`INT64`
`long`	`INT64`
`float`	`FLOAT64`
`double`	`FLOAT64`
`Decimal(P/S)`	`NUMERIC` or `BIG_NUMERIC` depending on precision
`date`	`DATE`
`time`	`TIME`
`timestamp (not partition column)`	`TIMESTAMP`
`timestamp (partition column)`	`DATETIME`
`string`	`STRING`
`binary`	`BYTES`
`array<Type>`	`ARRAY<Type>`
`struct`	`STRUCT`
`map<KeyType, ValueType>`	`ARRAY<Struct<key KeyType, value ValueType>>`

Limitations

The following limitations apply to Delta Lake tables:

External table limitations apply to Delta Lake tables.
Delta Lake tables are only supported on BigQuery Omni and have the associated limitations.
You can't update a table with a new JSON metadata file. You must use an auto detect schema table update operation. See Schema synchronization for more information.
BigLake security features only protect Delta Lake tables when accessed through BigQuery services.

Create a Delta Lake table

The following example creates an external table by using the CREATE EXTERNAL TABLE statement with the Delta Lake format:

CREATE [OR REPLACE] EXTERNAL TABLE table_name
WITH CONNECTION connection_name
OPTIONS (
         format = 'DELTA_LAKE',
         uris = ["parent_directory"]
       );

Replace the following:

table_name: The name of the table.
connection_name: The name of the connection. The connection must identify either an Amazon S3 or a Blob Storage source.
parent_directory: The URI of the parent directory.

BigQuery Omni transfer with Delta Lake

The following example uses the LOAD DATA statement to load data to the appropriate table:

LOAD DATA [INTO | OVERWRITE] table_name
FROM FILES (
        format = 'DELTA_LAKE',
        uris = ["parent_directory"]
)
WITH CONNECTION connection_name;

For more examples of BigQuery Omni data transfers, see Load data with BigQuery Omni operations.

Query BigLake tables

For more information, see Query Amazon S3 data.

View resource metadata

You can view the resource metadata with INFORMATION_SCHEMA views. When you query the JOBS_BY_*, JOBS_TIMELINE_BY_*, and RESERVATION* views, you must specify the query's processing location that is collocated with the table's region. For information about BigQuery Omni locations, see Locations. For all other system tables, specifying the query job location is optional.

For information about the system tables that BigQuery Omni supports, see Limitations.

To query JOBS_* and RESERVATION* system tables, select one of the following methods to specify the processing location:

Console

Go to the BigQuery page.

Go to BigQuery
If the Editor tab isn't visible, then click Compose new query.
Click More > Query settings. The Query settings dialog opens.
In the Query settings dialog, for Additional settings > Data location, select the BigQuery region that is collocated with the BigQuery Omni region. For example, if your BigQuery Omni region is aws-us-east-1, specify us-east4.
Select the remaining fields and click Save.

bq

Use the --location flag to set the job's processing location to the BigQuery region that is collocated with the BigQuery Omni region. For example, if your BigQuery Omni region is aws-us-east-1, specify us-east4.

Example

bq query --use_legacy_sql=false --location=us-east4 \
"SELECT * FROM region-aws-us-east-1.INFORMATION_SCHEMA.JOBS limit 10;"

bq query --use_legacy_sql=false --location=asia-northeast3 \
"SELECT * FROM region-aws-ap-northeast-2.INFORMATION_SCHEMA.JOBS limit 10;"

API

If you are running jobs programmatically, set the location argument to the BigQuery region that is collocated with the BigQuery Omni region. For example, if your BigQuery Omni region is aws-us-east-1, specify us-east4.

The following example lists the metadata refresh jobs in last six hours:

SELECT
 *
FROM
 `region-REGION_NAME`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE
 job_id LIKE '%metadata_cache_refresh%'
 AND creation_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 6 HOUR)
ORDER BY start_time desc
LIMIT 10;

Replace REGION_NAME with your region.

VPC Service Controls

You can use VPC Service Controls perimeters to restrict access from BigQuery Omni to an external cloud service as an extra layer of defense. For example, VPC Service Controls perimeters can limit exports from your BigQuery Omni tables to a specific Amazon S3 bucket or Blob Storage container.

To learn more about VPC Service Controls, see Overview of VPC Service Controls.

Required permission

Ensure that you have the required permissions to configure service perimeters. To view a list of IAM roles required to configure VPC Service Controls, see Access control with IAM in the VPC Service Controls documentation.

Set up VPC Service Controls using the Google Cloud console

In the Google Cloud console navigation menu, click Security, and then click VPC Service Controls.

Go to VPC Service Controls
To set up VPC Service Controls for BigQuery Omni, follow the steps in the Create a service perimeter guide, and when you are in the Egress rules pane, follow these steps:
1. In the Egress rules panel, click Add rule.
2. In the From attributes of the API client section, select an option from the Identity list.
3. Select To attributes of external resources.
4. To add an external resource, click Add external resources.
5. In the Add external resource dialog, for External resource name, enter a valid resource name. For example:
  - For Amazon Simple Storage Service (Amazon S3): s3://BUCKET_NAME
    
    Replace BUCKET_NAME with the name of your Amazon S3 bucket.
  - For Azure Blob Storage: azure://myaccount.blob.core.windows.net/CONTAINER_NAME
    
    Replace CONTAINER NAME with the name of your Blob Storage container.
  For a list of egress rule attributes, see Egress rules reference.
6. Select the methods that you want to allow on your external resources:
  1. If you want to allow all methods, select All methods in the Methods list.
  2. If you want to allow specific methods, select Selected method, click Select methods, and then select the methods that you want to allow on your external resources.
7. Click Create perimeter.

Set up VPC Service Controls using the gcloud CLI

To set up VPC Service Controls using the gcloud CLI, follow these steps:

Set the default access policy.
Create the egress policy input file.
Add the egress policy.

Set the default access policy

An access policy is an organization-wide container for access levels and service perimeters. For information about setting a default access policy or getting an access policy name, see Managing an access policy.

Create the egress policy input file

An egress rule block defines the allowed access from within a perimeter to resources outside of that perimeter. For external resources, the externalResources property defines the external resource paths allowed access from within your VPC Service Controls perimeter.

Egress rules can be configured using a JSON file, or a YAML file. The following sample uses the .yaml format:

- egressTo:
    operations:
    - serviceName: bigquery.googleapis.com
      methodSelectors:
      - method: "*"
      *OR*
      - permission: "externalResource.read"
    externalResources:
      - EXTERNAL_RESOURCE_PATH
  egressFrom:
    identityType: IDENTITY_TYPE
    *OR*
    identities:
    - serviceAccount:SERVICE_ACCOUNT

egressTo: lists allowed service operations on Google Cloud resources in specified projects outside the perimeter.
operations: list accessible services and actions or methods that a client satisfying the from block conditions is allowed to access.
serviceName: set bigquery.googleapis.com for BigQuery Omni.
methodSelectors: list methods that a client satisfying the from conditions can access. For restrictable methods and permissions for services, see Supported service method restrictions.
method : a valid service method, or \"*\" to allow all serviceName methods.
permission: a valid service permission, such as \"*\", externalResource.read, or externalResource.write. Access to resources outside the perimeter is allowed for operations that require this permission.
externalResources: lists external resources that clients inside a perimeter can access. Replace EXTERNAL_RESOURCE_PATH with either a valid Amazon S3 bucket, such as s3://bucket_name, or a Blob Storage container path, such as azure://myaccount.blob.core.windows.net/container_name.
egressFrom: lists allowed service operations on Google Cloud resources in specified projects within the perimeter.
identityType or identities: defines the identity types that can access the specified resources outside the perimeter. Replace IDENTITY_TYPE with one of the following valid values:
- ANY_IDENTITY: to allow all identities.
- ANY_USER_ACCOUNT: to allow all users.
- ANY_SERVICE_ACCOUNT: to allow all service accounts
identities: lists service accounts that can access the specified resources outside the perimeter.
serviceAccount (optional): replace SERVICE_ACCOUNT with the service account that can access the specified resources outside the perimeter.

Examples

The following example is a policy that allows egress operations from inside the perimeter to the s3://mybucket Amazon S3 location in AWS.

- egressTo:
    operations:
    - serviceName: bigquery.googleapis.com
      methodSelectors:
      - method: "*"
    externalResources:
      - s3://mybucket
      - s3://mybucket2
  egressFrom:
    identityType: ANY_IDENTITY

The following example allows egress operations to a Blob Storage container:

- egressTo:
    operations:
    - serviceName: bigquery.googleapis.com
      methodSelectors:
      - method: "*"
    externalResources:
      - azure://myaccount.blob.core.windows.net/mycontainer
  egressFrom:
    identityType: ANY_IDENTITY

For more information about egress policies, see the Egress rules reference.

Add the egress policy

To add the egress policy when you create a new service perimeter, use the gcloud access-context-manager perimeters create command. For example, the following command creates a new perimeter named omniPerimeter that includes the project with project number 12345, restricts the BigQuery API, and adds an egress policy defined in the egress.yaml file:

gcloud access-context-manager perimeters create omniPerimeter \
    --title="Omni Perimeter" \
    --resources=projects/12345 \
    --restricted-services=bigquery.googleapis.com \
    --egress-policies=egress.yaml

To add the egress policy to an existing service perimeter, use the gcloud access-context-manager perimeters update command. For example, the following command adds an egress policy defined in the egress.yaml file to an existing service perimeter named omniPerimeter:

gcloud access-context-manager perimeters update omniPerimeter
    --set-egress-policies=egress.yaml

Verify your perimeter

To verify the perimeter, use the gcloud access-context-manager perimeters describe command:

gcloud access-context-manager perimeters describe PERIMETER_NAME

Replace PERIMETER_NAME with the name of the perimeter.

For example, the following command describes the perimeter omniPerimeter:

gcloud access-context-manager perimeters describe omniPerimeter

For more information, see Managing service perimeters.

Allow BigQuery Omni VPC access to Amazon S3

As a BigQuery administrator, you can create an S3 bucket policy to grant BigQuery Omni access to your Amazon S3 resources. This ensures that only authorized BigQuery Omni VPCs can interact with your Amazon S3, enhancing the security of your data.

Apply an S3 bucket policy for BigQuery Omni VPC

To apply an S3 bucket policy, use the AWS CLI or Terraform:

AWS CLI

Run the following command to apply an S3 bucket policy that includes a condition using the aws:SourceVpc attribute:

  aws s3api put-bucket-policy \
    --bucket=BUCKET_NAME \
    --policy "{
      \"Version\": \"2012-10-17\",
      \"Id\": \"RestrictBucketReads\",
      \"Statement\": [
          {
              \"Sid\": \"AccessOnlyToOmniVPC\",
              \"Principal\": \"*\",
              \"Action\": [\"s3:ListBucket\", \"s3:GetObject\"],
              \"Effect\": \"Allow\",
              \"Resource\": [\"arn:aws:s3:::BUCKET_NAME\",
                             \"arn:aws:s3:::BUCKET_NAME/*\"],
              \"Condition\": {
                  \"StringEquals\": {
                    \"aws:SourceVpc\": \"VPC_ID\"
                  }
              }
          }
      ]
    }"

Replace the following:

BUCKET_NAME: the Amazon S3 bucket that you want BigQuery to access.
VPC_ID: the BigQuery Omni VPC ID of the BigQuery Omni region collocated with the Amazon S3 bucket. You can find this information in the table on this page.

Terraform

Add the following to your Terraform configuration file:

  resource "aws_s3_bucket" "example" {
    bucket = "BUCKET_NAME"
  }

  resource "aws_s3_bucket_policy" "example" {
    bucket = aws_s3_bucket.example.id
    policy = jsonencode({
      Version = "2012-10-17"
      Id      = "RestrictBucketReads"
      Statement = [
          {
              Sid       = "AccessOnlyToOmniVPC"
              Effect    = "Allow"
              Principal = "*"
              Action    = ["s3:GetObject", "s3:ListBucket"]
              Resource  = [
                  aws_s3_bucket.example.arn,
                  "${aws_s3_bucket.example.arn}/*"
                  ]
              Condition = {
                  StringEquals = {
                      "aws:SourceVpc": "VPC_ID"
                  }
              }
          },
      ]
    })
  }

Replace the following:

BUCKET_NAME: the Amazon S3 bucket that you want BigQuery to access.
VPC_ID: the BigQuery Omni VPC ID of the BigQuery Omni region collocated with the Amazon S3 bucket.

BigQuery Omni VPC Resource IDs

Region	VPC ID
aws-ap-northeast-2	vpc-0b488548024288af2
aws-ap-southeast-2	vpc-0726e08afef3667ca
aws-eu-central-1	vpc-05c7bba12ad45558f
aws-eu-west-1	vpc-0e5c646979bbe73a0
aws-us-east-1	vpc-0bf63a2e71287dace
aws-us-west-2	vpc-0cc24e567b9d2c1cb

Limitations

For a full list of limitations that apply to BigLake tables based on Amazon S3 and Blob Storage, see Limitations.

What's next

Learn about BigQuery Omni.
Use the BigQuery Omni with AWS lab.
Learn about BigLake tables.
Learn how to export query results to Amazon S3.
Learn how to create materialized views over Amazon Simple Storage Service (Amazon S3) metadata cache-enabled tables.
Learn how to make Amazon S3 data in a materialized view available locally for joins by creating a replica of the materialized view.

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Last updated 2026-06-03 UTC.