# Create a Bigtable external table A BigQuery permanent external table lets you query data stored in Bigtable by using GoogleSQL without first loading the data into BigQuery storage. You can then analyze Bigtable data alongside your other BigQuery data. This document describes how to create a Bigtable external table. Querying Bigtable data is available in all [Bigtable locations](https://docs.cloud.google.com/bigtable/docs/locations). ## Before you begin Before you create an external table, gather some information and make sure you have permission to create the table. ### Required roles To create an external table to use to query your Bigtable data, you must be a principal in the Bigtable Admin (`roles/bigtable.admin`) role for the instance that contains the source table. You also need the `bigquery.tables.create` BigQuery Identity and Access Management (IAM) permission. Each of the following predefined Identity and Access Management roles includes this permission: - BigQuery Data Editor (`roles/bigquery.dataEditor`) - BigQuery Data Owner (`roles/bigquery.dataOwner`) - BigQuery Admin (`roles/bigquery.admin`) If you are not a principal in any of these roles, ask your administrator to grant you access or to create the external table for you. For more information on Identity and Access Management roles and permissions in BigQuery, see [Predefined roles and permissions](https://docs.cloud.google.com/bigquery/docs/access-control). To view information on Bigtable permissions, see [Access control with Identity and Access Management](https://docs.cloud.google.com/bigtable/docs/access-control). To view the roles required to query the external table, see [Query Bigtable data](https://docs.cloud.google.com/bigquery/docs/external-data-bigtable). ### Create or identify a dataset Before you create an external table, you must [create a dataset](https://docs.cloud.google.com/bigquery/docs/datasets) to contain the external table. You can also use an existing dataset. ### Plan your compute usage > [!WARNING] > > **Preview** > > > This product or feature is > > subject to the "Pre-GA Offerings Terms" in the General Service Terms section > of the [Service Specific > Terms](https://docs.cloud.google.com/terms/service-terms#1). > > Pre-GA products and features are available "as is" and might have limited support. > > For more information, see the > [launch stage descriptions](https://cloud.google.com/products/#product-launch-stages). Determine the type of compute you want to use when you query your data. You specify that you want to use Data Boost or that you want to route to a dedicated cluster in your [app profile settings](https://docs.cloud.google.com/bigquery/docs/create-bigtable-external-table#app-profile). #### Data Boost To avoid impacting your application serving traffic, you can use Data Boost serverless compute when you use a BigQuery external table to read your Bigtable data. To use Data Boost, you must use a Data Boost app profile and include the app profile ID when you compose your Bigtable URI. For more information, see [Bigtable Data Boost overview](https://docs.cloud.google.com/bigtable/docs/data-boost-overview). > [!NOTE] > **Note:** While this standard usage is available in both the Enterprise and Enterprise Plus editions, using Data Boost specifically for SQL queries requires the Enterprise Plus edition. #### Provisioned nodes If you don't use Data Boost, cluster nodes are used for compute. If you don't use Data Boost and you plan to frequently query the same data that serves your production application, we recommend that you designate a cluster in your Bigtable instance to be used solely for BigQuery analysis. This isolates the traffic from the cluster or clusters that you use for your application's reads and writes. To learn more about replication and creating instances that have more than one cluster, see [About replication](https://docs.cloud.google.com/bigtable/docs/replication-overview). ### Identify or create an app profile Before you create an external table, decide which Bigtable app profile that BigQuery should use to read the data. We recommend that you use an app profile that you designate for use only with BigQuery. The app profile can be either a standard app profile or a Data Boost app profile, depending on the type of compute that you want to use to query your data. If you have a cluster in your Bigtable instance that is dedicated to BigQuery access, configure the app profile to use single-cluster routing to that cluster. To use Data Boost serverless compute, create a Data Boost app profile. To use cluster nodes for compute, create a standard app profile. To learn how Bigtable app profiles work, see [About app profiles](https://docs.cloud.google.com/bigtable/docs/app-profiles). To see how to create a new app profile, see [Create and configure app profiles](https://docs.cloud.google.com/bigtable/docs/configuring-app-profiles). ### Retrieve the Bigtable URI To create an external table for a Bigtable data source, you must provide the Bigtable URI. To retrieve the Bigtable URI, do the following: 1. Open the Bigtable page in the console. [Go to Bigtable](https://console.cloud.google.com/bigtable) 2. Retrieve the following details about your Bigtable data source: - Your project ID. - Your Bigtable instance ID. - The ID of the Bigtable app profile that you plan to use. This can be either a standard app profile or a Data Boost app profile, depending on the [type of compute](https://docs.cloud.google.com/bigquery/docs/create-bigtable-external-table#compute) that you want to use. If you don't specify an app profile ID, the default app profile is used. - The name of your Bigtable table. 3. Compose the Bigtable URI using the following format, where: - PROJECT_ID is the project that contains your Bigtable instance - INSTANCE_ID is the Bigtable instance ID - APP_PROFILE (optional) is the identifier for the app profile that you want to use - TABLE_NAME is the name of the table you're querying `https://googleapis.com/bigtable/projects/PROJECT_ID/instances/INSTANCE_ID[/appProfiles/APP_PROFILE]/tables/TABLE_NAME` > [!NOTE] > **Note:** Exactly one Bigtable URI can be specified, and it must be a fully specified, valid HTTPS URL for a Bigtable table. Wildcards are not supported for Bigtable external data sources. ## Create permanent external tables When you create a permanent external table in BigQuery that is linked to a Bigtable data source, there are two options for specifying the format of the external table: - If you are using the API or the bq command-line tool, you create a [table definition file](https://docs.cloud.google.com/bigquery/docs/external-table-definition) that defines the schema and metadata for the external table. - If you are using SQL, you use the `uri` option of the `CREATE EXTERNAL TABLE` statement to specify the Bigtable table to pull data from, and the `bigtable_options` option to specify the table schema. The external table data is not stored in the BigQuery table. Because the table is permanent, you can use dataset-level [access controls](https://docs.cloud.google.com/bigquery/docs/access-control) to share the table with others who also have access to the underlying Bigtable data source. To create a permanent table, choose one of the following methods. ### SQL You can create a permanent external table by running the [`CREATE EXTERNAL TABLE` DDL statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_external_table_statement). You must specify the table schema explicitly as part of the statement options. 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the query editor, enter the following statement: ```googlesql CREATE EXTERNAL TABLE DATASET.NEW_TABLE OPTIONS ( format = 'CLOUD_BIGTABLE', uris = ['URI'], bigtable_options = BIGTABLE_OPTIONS ); ``` Replace the following: - `DATASET`: the dataset in which to create the Bigtable external table. - `NEW_TABLE`: the name for the Bigtable external table. - `URI`: the URI for the Bigtable table you want to use as a data source. This URI must follow the format described in [Retrieving the Bigtable URI](https://docs.cloud.google.com/bigquery/docs/create-bigtable-external-table#bigtable-uri). - `BIGTABLE_OPTIONS`: the schema for the Bigtable table in JSON format. For a list of Bigtable table definition options, see `https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables#bigtableoptions` in the REST API reference.

3. Click **Run**.
For more information about how to run queries, see [Run an interactive query](https://docs.cloud.google.com/bigquery/docs/running-queries#queries). A statement to create an external Bigtable table might look similar to the following: CREATE EXTERNAL TABLE mydataset.BigtableTable OPTIONS ( format = 'CLOUD_BIGTABLE', uris = ['https://googleapis.com/bigtable/projects/myproject/instances/myBigtableInstance/appProfiles/myAppProfile/tables/table1'], bigtable_options = """ { columnFamilies: [ { "familyId": "familyId1", "type": "INTEGER", "encoding": "BINARY" } ], readRowkeyAsString: true } """ ); ### bq You create a table in the bq command-line tool using the [`bq mk` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#bq_mk). When you use the bq command-line tool to create a table linked to an external data source, you identify the table's schema using a [table definition file](https://docs.cloud.google.com/bigquery/docs/external-table-definition#tabledef-bigtable). 1. Use the `bq mk` command to create a permanent table. ```bash bq mk \ --external_table_definition=DEFINITION_FILE \ DATASET.TABLE ``` Replace the following: - `DEFINITION_FILE`: the path to the [table definition file](https://docs.cloud.google.com/bigquery/docs/external-table-definition#tabledef-bigtable) on your local machine. - `DATASET`: the name of the dataset that contains the table. - `TABLE`: the name of the table you're creating. ### API Use the [`tables.insert`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables/insert) API method, and create an [`ExternalDataConfiguration`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables#externaldataconfiguration) in the [`Table` resource](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table) that you pass in. For the `sourceUris` property in the `Table` resource, specify only one [Bigtable URI](https://docs.cloud.google.com/bigquery/docs/create-bigtable-external-table#bigtable-uri). It must be a valid HTTPS URL. For the `sourceFormat` property, specify `"BIGTABLE"`. ### Java Before trying this sample, follow the Java setup instructions in the [BigQuery quickstart using client libraries](https://docs.cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries). For more information, see the [BigQuery Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/overview). To authenticate to BigQuery, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication#client-libs). import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQuery.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryException.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryOptions.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumnFamily.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableOptions.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.ExternalTableDefinition.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.QueryJobConfiguration.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.TableId.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.TableInfo.html; import com.google.cloud.bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.TableResult.html; import com.google.common.collect.ImmutableList; import org.apache.commons.codec.binary.Base64; // Sample to queries an external bigtable data source using a permanent table public class QueryExternalBigtablePerm { public static void main(String[] args) { // TODO(developer): Replace these variables before running the sample. String projectId = "MY_PROJECT_ID"; String bigtableInstanceId = "MY_INSTANCE_ID"; String bigtableTableName = "MY_BIGTABLE_NAME"; String bigqueryDatasetName = "MY_DATASET_NAME"; String bigqueryTableName = "MY_TABLE_NAME"; String sourceUri = String.format( "https://googleapis.com/bigtable/projects/%s/instances/%s/tables/%s", projectId, bigtableInstanceId, bigtableTableName); String query = String.format("SELECT * FROM %s ", bigqueryTableName); queryExternalBigtablePerm(bigqueryDatasetName, bigqueryTableName, sourceUri, query); } public static void queryExternalBigtablePerm( String datasetName, String tableName, String sourceUri, String query) { 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. https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQuery.html bigquery = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryOptions.html.getDefaultInstance().getService(); https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumnFamily.html.Builder statsSummary = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumnFamily.html.newBuilder(); // Configuring Columns https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.html connectedCell = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.html.newBuilder() .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.Builder.html#com_google_cloud_bigquery_BigtableColumn_Builder_setQualifierEncoded_java_lang_String_(Base64.encodeBase64String("connected_cell".https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery-jdbc/latest/com.google.cloud.bigquery.jdbc.BigQueryBaseResultSet.html#com_google_cloud_bigquery_jdbc_BigQueryBaseResultSet_getBytes_int_())) .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.Builder.html#com_google_cloud_bigquery_BigtableColumn_Builder_setFieldName_java_lang_String_("connected_cell") .setType("STRING") .setEncoding("TEXT") .build(); https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.html connectedWifi = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.html.newBuilder() .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.Builder.html#com_google_cloud_bigquery_BigtableColumn_Builder_setQualifierEncoded_java_lang_String_(Base64.encodeBase64String("connected_wifi".https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery-jdbc/latest/com.google.cloud.bigquery.jdbc.BigQueryBaseResultSet.html#com_google_cloud_bigquery_jdbc_BigQueryBaseResultSet_getBytes_int_())) .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.Builder.html#com_google_cloud_bigquery_BigtableColumn_Builder_setFieldName_java_lang_String_("connected_wifi") .setType("STRING") .setEncoding("TEXT") .build(); https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.html osBuild = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.html.newBuilder() .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.Builder.html#com_google_cloud_bigquery_BigtableColumn_Builder_setQualifierEncoded_java_lang_String_(Base64.encodeBase64String("os_build".https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery-jdbc/latest/com.google.cloud.bigquery.jdbc.BigQueryBaseResultSet.html#com_google_cloud_bigquery_jdbc_BigQueryBaseResultSet_getBytes_int_())) .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumn.Builder.html#com_google_cloud_bigquery_BigtableColumn_Builder_setFieldName_java_lang_String_("os_build") .setType("STRING") .setEncoding("TEXT") .build(); // Configuring column family and columns statsSummary .setColumns(ImmutableList.of(connectedCell, connectedWifi, osBuild)) .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableColumnFamily.Builder.html#com_google_cloud_bigquery_BigtableColumnFamily_Builder_setFamilyID_java_lang_String_("stats_summary") .setOnlyReadLatest(true) .setEncoding("TEXT") .setType("STRING") .build(); // Configuring BigtableOptions is optional. https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableOptions.html options = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableOptions.html.newBuilder() .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableOptions.Builder.html#com_google_cloud_bigquery_BigtableOptions_Builder_setIgnoreUnspecifiedColumnFamilies_java_lang_Boolean_(true) .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableOptions.Builder.html#com_google_cloud_bigquery_BigtableOptions_Builder_setReadRowkeyAsString_java_lang_Boolean_(true) .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigtableOptions.Builder.html#com_google_cloud_bigquery_BigtableOptions_Builder_setColumnFamilies_java_util_List_com_google_cloud_bigquery_BigtableColumnFamily__(ImmutableList.of(statsSummary.build())) .build(); https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.TableId.html tableId = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.TableId.html.of(datasetName, tableName); // Create a permanent table linked to the Bigtable table https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.ExternalTableDefinition.html externalTable = https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.ExternalTableDefinition.html.newBuilder(sourceUri, options).build(); bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQuery.html#com_google_cloud_bigquery_BigQuery_create_com_google_cloud_bigquery_DatasetInfo_com_google_cloud_bigquery_BigQuery_DatasetOption____(TableInfo.of(tableId, externalTable)); // Example query https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.TableResult.html results = bigquery.https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQuery.html#com_google_cloud_bigquery_BigQuery_query_com_google_cloud_bigquery_QueryJobConfiguration_com_google_cloud_bigquery_BigQuery_JobOption____(https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.QueryJobConfiguration.html.of(query)); results .https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.TableResult.html#com_google_cloud_bigquery_TableResult_iterateAll__() .forEach(row -> row.forEach(val -> System.out.printf("%s,", val.toString()))); System.out.println("Query on external permanent table performed successfully."); } catch (https://docs.cloud.google.com/java/docs/reference/google-cloud-bigquery/latest/com.google.cloud.bigquery.BigQueryException.html | InterruptedException e) { System.out.println("Query not performed \n" + e.toString()); } } } ## Query external tables For more information, see [Query Bigtable data](https://docs.cloud.google.com/bigquery/docs/external-data-bigtable). ## Generated schema By default, BigQuery exposes the values in a column family as an array of columns and within that, an array of values written at different timestamps. This schema preserves the natural layout of data in Bigtable, but SQL queries can be challenging. It is possible to promote columns to subfields within the parent column family and to read only the latest value from each cell. This represents both of the arrays in the default schema as scalar values. ### Example You are storing user profiles for a fictional social network. One data model for this might be a `profile` column family with individual columns for `gender`, `age` and `email`: rowkey | profile:gender| profile:age| profile:email ---| ---| ---| --- alice | female | 30 | alice@gmail.com Using the default schema, a GoogleSQL query to count the number of male users over 30 is: ```googlesql SELECT COUNT(1) FROM `dataset.table` OMIT RECORD IF NOT SOME(profile.column.name = "gender" AND profile.column.cell.value = "male") OR NOT SOME(profile.column.name = "age" AND INTEGER(profile.column.cell.value) > 30) ``` Querying the data is less challenging if `gender` and `age` are exposed as sub- fields. To expose them as sub-fields, list `gender` and `age` as named columns in the `profile` column family when defining the table. You can also instruct BigQuery to expose the latest values from this column family because typically, only the latest value (and possibly the only value) is of interest. After exposing the columns as sub-fields, the GoogleSQL query to count the number of male users over 30 is: ```googlesql SELECT COUNT(1) FROM `dataset.table` WHERE profile.gender.cell.value="male" AND profile.age.cell.value > 30 ``` Notice how `gender` and `age` are referenced directly as fields. The JSON configuration for this setup is: ```json "bigtableOptions": { "readRowkeyAsString": "true", "columnFamilies": [ { "familyId": "profile", "onlyReadLatest": "true", "columns": [ { "qualifierString": "gender", "type": "STRING" }, { "qualifierString": "age", "type": "INTEGER" } ] } ] } ``` ## Value encoding Bigtable stores data as raw bytes, independent to data encoding. However, byte values are of limited use in SQL query analysis. Bigtable provides two basic types of scalar decoding: text and HBase-binary. The text format assumes that all values are stored as alphanumeric text strings. For example, an integer 768 will be stored as the string "768". The binary encoding assumes that HBase's [`Bytes.toBytes`](https://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/util/Bytes.html) class of methods were used to encode the data and applies an appropriate decoding method. ## Supported regions and zones Querying data in Bigtable is available in all supported Bigtable zones. For more information, see [BigQuery locations](https://docs.cloud.google.com/bigtable/docs/locations). For multi-cluster instances, BigQuery routes traffic based on Bigtable [app profile](https://docs.cloud.google.com/bigtable/docs/app-profiles) settings. ## Limitations - You can't create external tables over Bigtable SQL-based objects, such as views and continuous materialized views. - For more information about limitations that apply to external tables, see [External table limitations](https://docs.cloud.google.com/bigquery/docs/external-tables#limitations). ## Scopes for Compute Engine instances When you create a Compute Engine instance, you can specify a list of scopes for the instance. The scopes control the instance's access to Google Cloud products, including Bigtable. Applications running on the VM use the service account to call Google Cloud APIs. If you set up a Compute Engine instance to run as a [service account](https://docs.cloud.google.com/compute/docs/access/create-enable-service-accounts-for-instances), and that service account accesses an external table linked to a Bigtable data source, you must add the Bigtable read-only data access scope (`https://www.googleapis.com/auth/bigtable.data.readonly`) to the instance. For more information, see [Creating a Compute Engine instance for Bigtable](https://docs.cloud.google.com/bigtable/docs/creating-compute-instance). For information on applying scopes to a Compute Engine instance, see [Changing the service account and access scopes for an instance](https://docs.cloud.google.com/compute/docs/access/create-enable-service-accounts-for-instances#changeserviceaccountandscopes). For more information on Compute Engine service accounts, see [Service accounts](https://docs.cloud.google.com/compute/docs/access/service-accounts). ## What's next - [Learn more about Bigtable schema design.](https://docs.cloud.google.com/bigtable/docs/schema-design) - [Review the introduction to external data sources.](https://docs.cloud.google.com/bigquery/docs/external-data-sources)