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 Azure 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 theazure-eastus2 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='azure-eastus2');

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.

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, andthen 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, selectAzure Blob Storage.

   2. ForSelect Azure Blob Storage path, enter a Blob Storagepath using the following format:azure://AZURE_STORAGE_ACCOUNT_NAME.blob.core.windows.net/CONTAINER_NAME/FILE_PATH

      Replace the following:

      • AZURE_STORAGE_ACCOUNT_NAME: The name ofthe Blob Storage account. The account's region should be the sameas the dataset's region.
      • CONTAINER_NAME: The name of the Blob Storagecontainer.
      • FILE_PATH: The data path that points tothe Blob Storage data. For example, for a single CSV file,FILE_PATH can bemyfile.csv.

   3. ForFile format, select the data format in Azure. Supported formatsareAVRO,CSV,DELTA_LAKE,ICEBERG,JSONL,ORC, andPARQUET.

6. In theDestination section, do the following:

   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 toBlob Storage.

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 TABLEstatement 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`AZURE_LOCATION.CONNECTION_NAME`OPTIONS(format='DATA_FORMAT',uris=['azure://AZURE_STORAGE_ACCOUNT_NAME.blob.core.windows.net/CONTAINER_NAME/FILE_PATH']);

   Replace the following:

   • DATASET_NAME: the name of the dataset you created
   • TABLE_NAME: the name you want to give to this table
   • AZURE_LOCATION: an Azure location in Google Cloud, such asazure-eastus2
   • CONNECTION_NAME: the name of the connection you created
   • DATA_FORMAT: any of the supportedBigQuery federated formats, such asAVRO,CSV,DELTA_LAKE, orICEBERG (preview)
   • AZURE_STORAGE_ACCOUNT_NAME: the name of the Blob Storage account
   • CONTAINER_NAME: the name of the Blob Storage container
   • FILE_PATH: the data path that points to the Blob Storage data

3. Clickplay_circleRun.

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

Example:

CREATEEXTERNALTABLEabsdataset.abstableWITHCONNECTION`azure-eastus2.abs-read-conn`OPTIONS(format='CSV',uris=['azure://account_name.blob.core.windows.net/container/path/file.csv']);

bq

Create atable definition file:

bqmkdef\--source_format=DATA_FORMAT\--connection_id=AZURE_LOCATION.CONNECTION_NAME\"azure://AZURE_STORAGE_ACCOUNT_NAME.blob.core.windows.net/CONTAINER_NAME/FILE_PATH">table_def

Replace the following:

• DATA_FORMAT: any of the supportedBigQuery federated formats,such asAVRO,CSV,ICEBERG, orPARQUET
• AZURE_LOCATION: an Azure location in Google Cloud,such asazure-eastus2
• CONNECTION_NAME: the name of the connection that youcreated
• AZURE_STORAGE_ACCOUNT_NAME: the name of the Blob Storage account
• CONTAINER_NAME: the name of the Blob Storage container
• FILE_PATH: the data path that points to the Blob Storage data

Next, create the BigLake table:

bqmk--external_table_definition=table_defDATASET_NAME.TABLE_NAME

Replace the following:

• DATASET_NAME: the name of the dataset that you created
• TABLE_NAME: the name that you want to give to this table

For example, the following commands create a new BigLake table,my_dataset.my_table, which can query your Blob Storage data that's storedat the pathazure://account_name.blob.core.windows.net/container/path andhas a read connection in the locationazure-eastus2:

bqmkdef\--source_format=AVRO\--connection_id=azure-eastus2.read-conn\"azure://account_name.blob.core.windows.net/container/path">table_defbqmk\--external_table_definition=table_defmy_dataset.my_table

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 Blob Storage.

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, andthen select a dataset.

4. ClickCreate table. TheCreate table pane opens.

5. In theSource section, specify the following details:

   1. ForCreate table from, select one of the following options:

      • Amazon S3
      • Azure Blob Storage

   2. Provide the path to the folder, usingwildcards.For example:

      • For Amazon S3:s3://mybucket/*
      • For Blob Storage:azure://mystorageaccount.blob.core.windows.net/mycontainer/*

      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");

   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/
     • azure://mystorageaccount.blob.core.windows.net/mycontainer/
   • FILE_PATH: path to the data source for the external table that you want to create—for example:
     • s3://mybucket/*.parquet
     • azure://mystorageaccount.blob.core.windows.net/mycontainer/*.parquet
   • TABLE_FORMAT: the format of the table that you want to create—for example,PARQUET

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/*']);

The following example creates a BigLake table overpartitioned data in Blob Storage. The schema is specified.

CREATEEXTERNALTABLE`my_dataset.my_table`(ProductIdINTEGER,ProductName,STRING,ProductType,STRING)WITHPARTITIONCOLUMNS(skuSTRING,)WITHCONNECTION`us.my-connection`OPTIONS(hive_partition_uri_prefix="azure://mystorageaccount.blob.core.windows.net/mycontainer/products",uris=['azure://mystorageaccount.blob.core.windows.net/mycontainer/*']);

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\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.
• URIS: the path to the Amazon S3 or theBlob Storage 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=GCS_URI_SHARED_PREFIX/{KEY1:TYPE1}/{KEY2:TYPE2}/...

After you create the table definition file, use thebq mk command tocreate the BigLake table:

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

Replace the following:

• 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 --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 \  s3://mybucket/myTable/* > mytable_defbq mk --external_table_definition=mytable_def \  mydataset.mytable \  Region:STRING,Quarter:STRING,Total_sales:INTEGER

The following example usesCUSTOM Hive partitioning mode forBlob Storage data:

bq mkdef --source_format=CSV \  --connection_id=us.my-connection \  --hive_partitioning_mode=CUSTOM \  --hive_partitioning_source_uri_prefix=azure://mystorageaccount.blob.core.windows.net/mycontainer/{dt:DATE}/{val:STRING} \  azure://mystorageaccount.blob.core.windows.net/mycontainer/* > mytable_defbq mk --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-azure-eastus2.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.

Azure PowerShell

Run the following command to add a network rule to your storage account thatspecifies the retrieved BigQuery Omni subnet IDs as theVirtualNetworkResourceId.

Add-AzStorageAccountNetworkRule`-ResourceGroupName"RESOURCE_GROUP_NAME"`-Name"STORAGE_ACCOUNT_NAME"`-VirtualNetworkResourceId"SUBNET_ID1","SUBNET_ID2"

Replace the following:

• RESOURCE_GROUP_NAME: the resource group name.
• STORAGE_ACCOUNT_NAME: the storage account name.
• SUBNET_ID1,SUBNET_ID1:the subnet IDs. You can find this information in the table on this page.

Terraform

Add the following to your Terraform configuration file:

resource"azurerm_storage_account_network_rules""example"{storage_account_name="STORAGE_ACCOUNT_NAME"resource_group_name="RESOURCE_GROUP_NAME"default_action="Allow"bypass=["Logging", "Metrics", "AzureServices"]virtual_network_subnet_ids=["SUBNET_ID1","SUBNET_ID2"]}

Replace the following:

• STORAGE_ACCOUNT_NAME: the storage account name.
• RESOURCE_GROUP_NAME: the resource group name.
• SUBNET_ID1,SUBNET_ID1:the subnet IDs. You can find this information in the table on this page.