Console

1. Go to theBigQuery page.

   Go to BigQuery

2. In the left pane, clickexploreExplorer.
3. In theExplorer pane, select the project where you want to create the dataset.
4. Clickmore_vertView actions, and then clickCreate dataset.
5. On theCreate dataset page, specify the following details:
1. ForDataset ID enter a unique datasetname.
2. ForData location choose asupported region.
3. Optional: To delete tables automatically, select theEnable table expiration checkbox and set theDefault maximum table age in days. Data in Amazon S3 is not deleted when the table expires.
4. If you want to usedefault collation, expand theAdvanced options section and then select theEnable default collation option.
5. ClickCreate dataset.

SQL

Use theCREATE SCHEMA DDL statement.The following example create a dataset in theaws-us-east-1 region:

1. In the Google Cloud console, go to theBigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   CREATESCHEMAmydatasetOPTIONS(location='aws-us-east-1');

3. Clickplay_circleRun.

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

bq

In a command-line environment, create a dataset using thebq mkcommand:

bq--location=LOCATIONmk\--dataset\PROJECT_ID:DATASET_NAME

The--project_id parameter overrides the default project.

Replace the following:

• LOCATION: the location of your dataset

  For information about supported regions, seeLocations.After youcreate a dataset, you can't change its location. You can set a defaultvalue for the location by using the.bigqueryrc file.

• PROJECT_ID: your project ID

• DATASET_NAME: the name of the dataset thatyou want to create

  To create a dataset in a project other than your default project, add theproject ID to the dataset name in the following format:PROJECT_ID:DATASET_NAME.

Java

importcom.google.cloud.bigquery.BigQuery;importcom.google.cloud.bigquery.BigQueryException;importcom.google.cloud.bigquery.BigQueryOptions;importcom.google.cloud.bigquery.Dataset;importcom.google.cloud.bigquery.DatasetInfo;// Sample to create a aws datasetpublicclassCreateDatasetAws{publicstaticvoidmain(String[]args){// TODO(developer): Replace these variables before running the sample.StringprojectId="MY_PROJECT_ID";StringdatasetName="MY_DATASET_NAME";// Note: As of now location only supports aws-us-east-1Stringlocation="aws-us-east-1";createDatasetAws(projectId,datasetName,location);}publicstaticvoidcreateDatasetAws(StringprojectId,StringdatasetName,Stringlocation){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.BigQuerybigquery=BigQueryOptions.getDefaultInstance().getService();DatasetInfodatasetInfo=DatasetInfo.newBuilder(projectId,datasetName).setLocation(location).build();Datasetdataset=bigquery.create(datasetInfo);System.out.println("Aws dataset created successfully :"+dataset.getDatasetId().getDataset());}catch(BigQueryExceptione){System.out.println("Aws dataset was not created. \n"+e.toString());}}}

Console

1. Go to theBigQuery page.

   Go to BigQuery

2. In the left pane, clickexploreExplorer:

   

   If you don't see the left pane, clicklast_pageExpand left pane to open the pane.

3. In theExplorer pane, expand your project, clickDatasets, and then select a dataset.

4. In theDataset info section, clickadd_boxCreate table.

5. On theCreate table page, in theSource section, do the following:

   1. ForCreate table from, selectAmazon S3.
   2. ForSelect S3 path, enter a URI pointing to the Amazon S3data in the formats3://BUCKET_NAME/PATH. ReplaceBUCKET_NAME with the name of the Amazon S3 bucket;the bucket's region should be the same as the dataset's region.ReplacePATH with the path that you would liketo write the exported file to; it can contain one wildcard*.
   3. ForFile format, select the data format in Amazon S3. Supported formatsareAVRO,CSV,DELTA_LAKE,ICEBERG,JSONL,ORC, andPARQUET.

6. In theDestination section, specify thefollowing details:

   1. ForDataset, choose the appropriate dataset.
   2. In theTable field, enter the name of the table.
   3. Verify thatTable type is set toExternal table.
   4. ForConnection ID, choose the appropriate connection ID from thedrop-down. For information about connections, seeConnect to Amazon S3.

7. In theSchema section, you can either enableschema auto-detection or manually specifya schema if you have a source file. If you don't have a source file, youmust manually specify a schema.

   • To enable schema auto-detection, select theAuto-detect option.

   • To manually specify a schema, leave theAuto-detect optionunchecked. EnableEdit as text and enter the table schema as aJSON array.

8. ClickCreate table.

SQL

To create a BigLake table, use theCREATE EXTERNAL TABLE statement with theWITH CONNECTION clause:

1. In the Google Cloud console, go to theBigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   CREATEEXTERNALTABLEDATASET_NAME.TABLE_NAMEWITHCONNECTION`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 supportedBigQuery federated formats (such asAVRO,CSV,DELTA_LAKE,ICEBERG, orPARQUET (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, seeMetadata caching for performance.

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

     To enable metadata caching, specify aninterval literal value between 30 minutes and 7 days. For example, specifyINTERVAL 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, seeMetadata caching for performance.

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

     Set toMANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call theBQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.

     You must setCACHE_MODE ifSTALENESS_INTERVAL is set to a value greater than 0.

3. Clickplay_circleRun.

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

Example:

CREATEEXTERNALTABLEawsdataset.awstableWITHCONNECTION`aws-us-east-1.s3-read-connection`OPTIONS(format="CSV",uris=["s3://s3-bucket/path/file.csv"],max_staleness=INTERVAL1DAY,metadata_cache_mode='AUTOMATIC');

bq

Create atable definition file:

bqmkdef\--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 supportedBigQuery federated formats(such asAVRO,CSV,DELTA_LAKE,ICEBERG, orPARQUET).
• S3_URI: a URI pointing to the Amazon S3 data (for example,s3://bucket/path).
• AWS_LOCATION: an AWS location in Google Cloud (forexample,aws-us-east-1).

• CONNECTION_NAME: the name of the connection youcreated.

• CACHE_MODE: specifies whether the metadatacache is refreshed automatically or manually. You only need to includethis flag if you also plan to use the--max_staleness flagin the subsequentbq mk command to enable metadata caching.For more information about metadata caching considerations, seeMetadata caching for performance.

  Set toAUTOMATIC for the metadata cache to berefreshed at a system-defined interval, usually somewhere between 30 and60 minutes.

  Set toMANUAL if you want to refreshthe metadata cache on a schedule you determine. In this case, you can calltheBQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.You must setCACHE_MODE ifSTALENESS_INTERVAL is set to a value greaterthan 0.

bqmk--max_staleness=STALENESS_INTERVAL--external_table_definition=table_defDATASET_NAME.TABLE_NAME

• STALENESS_INTERVAL: specifies whethercached metadata is used by operations against the BigLaketable, and how fresh the cached metadata must be in order for theoperation to use it. For more information about metadata cachingconsiderations, seeMetadata caching for performance.

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

  To enable metadata caching, specify aninterval literalvalue between 30 minutes and 7 days. For example, specifyINTERVAL 4 HOUR for a 4 hour staleness interval.With this value, operations against the table use cached metadata ifit has been refreshed within the past 4 hours. If the cached metadatais older than that, the operation retrieves metadata fromAmazon 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 storedat the paths3://s3-bucket/path/file.csv and has a read connection in thelocationaws-us-east-1:

bqmkdef\--autodetect\--source_format=CSV\--connection_id=aws-us-east-1.s3-read-connection\--metadata_cache_mode=AUTOMATIC\s3://s3-bucket/path/file.csv>table_defbqmk--max_staleness=INTERVAL"1"HOUR\--external_table_definition=table_defawsdataset.awstable

API

Call thetables.insert methodAPI method, and create anExternalDataConfigurationin theTable resourcethat you pass in.

Specify theschema property or set theautodetect property totrue to enable schema auto detection forsupported data sources.

Specify theconnectionId property to identify the connection to usefor connecting to Amazon S3.

Java

importcom.google.cloud.bigquery.BigQuery;importcom.google.cloud.bigquery.BigQueryException;importcom.google.cloud.bigquery.BigQueryOptions;importcom.google.cloud.bigquery.CsvOptions;importcom.google.cloud.bigquery.ExternalTableDefinition;importcom.google.cloud.bigquery.Field;importcom.google.cloud.bigquery.Schema;importcom.google.cloud.bigquery.StandardSQLTypeName;importcom.google.cloud.bigquery.TableId;importcom.google.cloud.bigquery.TableInfo;// Sample to create an external aws tablepublicclassCreateExternalTableAws{publicstaticvoidmain(String[]args){// TODO(developer): Replace these variables before running the sample.StringprojectId="MY_PROJECT_ID";StringdatasetName="MY_DATASET_NAME";StringtableName="MY_TABLE_NAME";StringconnectionId="MY_CONNECTION_ID";StringsourceUri="s3://your-bucket-name/";CsvOptionsoptions=CsvOptions.newBuilder().setSkipLeadingRows(1).build();Schemaschema=Schema.of(Field.of("name",StandardSQLTypeName.STRING),Field.of("post_abbr",StandardSQLTypeName.STRING));ExternalTableDefinitionexternalTableDefinition=ExternalTableDefinition.newBuilder(sourceUri,options).setConnectionId(connectionId).setSchema(schema).build();createExternalTableAws(projectId,datasetName,tableName,externalTableDefinition);}publicstaticvoidcreateExternalTableAws(StringprojectId,StringdatasetName,StringtableName,ExternalTableDefinitionexternalTableDefinition){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.BigQuerybigquery=BigQueryOptions.getDefaultInstance().getService();TableIdtableId=TableId.of(projectId,datasetName,tableName);TableInfotableInfo=TableInfo.newBuilder(tableId,externalTableDefinition).build();bigquery.create(tableInfo);System.out.println("Aws external table created successfully");// Clean upbigquery.delete(TableId.of(projectId,datasetName,tableName));}catch(BigQueryExceptione){System.out.println("Aws external was not created."+e.toString());}}}

Console

1. Go to theBigQuery page.

   Go to BigQuery

2. In the left pane, clickexploreExplorer:

   

3. In theExplorer pane, expand your project, clickDatasets, andselect a dataset.

4. ClickCreate table. This opens theCreate table pane.

5. In theSource section, specify the following details:

   1. ForCreate table from, selectAmazon S3.

   2. Provide the path to the folder, usingwildcards.For example,s3://mybucket/*.

      The foldermust be in the same location as the dataset that contains thetable you want to create, append, or overwrite.

   3. From theFile format list, select the file type.

   4. Select theSource data partitioning checkbox, and then specifythe following details:

      1. ForSelect Source URI Prefix, enter theURI prefix. For example,s3://mybucket/my_files.
      2. Optional: To require a partition filter on all queries for thistable, select theRequire partition filter checkbox.Requiring a partition filter can reduce cost and improveperformance. For more information, seeRequiring predicate filters on partition keys in queries.

      3. In thePartition inference mode section, select one of thefollowing options:

         • Automatically infer types: set the partition schemadetection mode toAUTO.
         • All columns are strings: set the partition schemadetection mode toSTRINGS.
         • Provide my own: set the partition schema detection mode toCUSTOM and manually enter the schemainformation for the partition keys. For more information, seeCustom partition key schema.

6. In theDestination section, specify the following details:

   1. ForProject, select the project in which you want to createthe table.
   2. ForDataset, select the dataset in which you want to createthe table.
   3. ForTable, enter the name of the table that you wantto create.
   4. ForTable type, verify thatExternal table is selected.
   5. ForConnection ID, select the connection that you createdearlier.

7. In theSchema section, you can either enableschema auto-detection or manually specifya schema if you have a source file. If you don't have a source file, youmust manually specify a schema.

   • To enable schema auto-detection, select theAuto-detect option.

   • To manually specify a schema, leave theAuto-detect optionunchecked. EnableEdit as text and enter the table schema as aJSON array.

8. To ignore rows with extra column values that don't match the schema,expand theAdvanced options section and selectUnknown values.

9. ClickCreate table.

SQL

Use theCREATE EXTERNAL TABLE DDL statement:

1. In the Google Cloud console, go to theBigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   CREATEEXTERNALTABLE`PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME`WITHPARTITIONCOLUMNS(PARTITION_COLUMNPARTITION_COLUMN_TYPE,)WITHCONNECTION`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 partitioninguri 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, seeMetadata caching for performance.

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

     To enable metadata caching, specify aninterval literal value between 30 minutes and 7 days. For example, specifyINTERVAL 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, seeMetadata caching for performance.

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

     Set toMANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call theBQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.

     You must setCACHE_MODE ifSTALENESS_INTERVAL is set to a value greater than 0.

3. Clickplay_circleRun.

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

Examples

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

CREATEEXTERNALTABLE`my_dataset.my_table`WITHPARTITIONCOLUMNS(skuSTRING,)WITHCONNECTION`us.my-connection`OPTIONS(hive_partition_uri_prefix="s3://mybucket/products",uris=['s3://mybucket/products/*']max_staleness=INTERVAL1DAY,metadata_cache_mode='AUTOMATIC');

bq

First, use thebq mkdef command tocreate a table definition file:

bqmkdef\--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 datasource. For example,CSV.
• REGION: the region that contains theconnection—for example,us.
• CONNECTION_ID: the name of the connection—forexample,myconnection.
• PARTITIONING_MODE: the Hive partitioning mode. Use one of thefollowing 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 querytime. This flag is optional. The default value isfalse.

• CACHE_MODE: specifies whether the metadatacache is refreshed automatically or manually. You only need to includethis flag if you also plan to use the--max_staleness flagin the subsequentbq mk command to enable metadata caching.For more information about metadata caching considerations, seeMetadata caching for performance.

  Set toAUTOMATIC for the metadata cache to berefreshed at a system-defined interval, usually somewhere between 30 and60 minutes.

  Set toMANUAL if you want to refreshthe metadata cache on a schedule you determine. In this case, you can calltheBQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.You must setCACHE_MODE ifSTALENESS_INTERVAL is set to a value greaterthan 0.

• URIS: the path to the Amazon S3 folder, usingwildcard format.

• DEFINITION_FILE: the path to thetable definition file on your localmachine.

IfPARTITIONING_MODE isCUSTOM, include the partition key schemain 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 thebq mk command tocreate the BigLake table:

bqmk--max_staleness=STALENESS_INTERVAL\--external_table_definition=DEFINITION_FILE\DATASET_NAME.TABLE_NAME\SCHEMA

Replace the following:

• STALENESS_INTERVAL: specifies whethercached metadata is used by operations against the BigLaketable, and how fresh the cached metadata must be in order for theoperation to use it. For more information about metadata cachingconsiderations, seeMetadata caching for performance.

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

  To enable metadata caching, specify aninterval literalvalue between 30 minutes and 7 days. For example, specifyINTERVAL 4 HOUR for a 4 hour staleness interval.With this value, operations against the table use cached metadata ifit has been refreshed within the past 4 hours. If the cached metadatais older than that, the operation retrieves metadata fromAmazon S3 instead.

• DEFINITION_FILE: the path to the table definition file.

• DATASET_NAME: the name of the dataset that contains thetable.

• TABLE_NAME: the name of the table you're creating.

• SCHEMA: specifies a path to aJSON schema file,or specifies the schema in the formfield:data_type,field:data_type,.... To use schemaauto-detection, omit this argument.

Examples

The following example usesAUTO Hive partitioning mode for Amazon S3data:

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

The following example usesSTRING Hive partitioning mode for Amazon S3data:

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_defbq 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 thehivePartitioningOptionsobject in theExternalDataConfigurationobject when you create thetable definition file.To create a BigLake table, you must also specify a valuefor theconnectionId field.

If you set thehivePartitioningOptions.mode field toCUSTOM, you mustencode the partition key schema in thehivePartitioningOptions.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 thehivePartitioningOptions.requirePartitionFilter field totrue.

Console

1. Go to theBigQuery page.

   Go to BigQuery

2. If theEditor tab isn't visible, then clickadd_boxCompose new query.

3. ClickMore>Query settings. TheQuery settingsdialog opens.

4. In theQuery settings dialog, forAdditional settings>Data location, select theBigQuery regionthat is collocated with the BigQuery Omni region.For example, if your BigQuery Omni region isaws-us-east-1,specifyus-east4.

5. Select the remaining fields and clickSave.

bq

Use the--location flag to set the job's processing location to theBigQuery region that iscollocated with the BigQuery Omni region.For example, if your BigQuery Omni region isaws-us-east-1,specifyus-east4.

Example

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

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

API

If you arerunning jobs programmatically,set the location argument to theBigQuery regionthat is collocated with the BigQuery Omni region.For example, if your BigQuery Omni region isaws-us-east-1,specifyus-east4.

AWS CLI

Run the following command to apply an S3 bucket policy that includes acondition using theaws:SourceVpc attribute:

awss3apiput-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 thatyou want BigQuery to access.
• VPC_ID: the BigQuery Omni VPC ID of theBigQuery Omni region collocated with the Amazon S3bucket. 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.idpolicy=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 thatyou want BigQuery to access.
• VPC_ID: the BigQuery Omni VPC ID of theBigQuery Omni region collocated with the Amazon S3bucket.