# Load data with BigQuery Omni operations BigQuery Omni lets you load cross-cloud data from Amazon Simple Storage Service (Amazon S3) buckets and Azure Blob Storage into [BigQuery tables](https://docs.cloud.google.com/bigquery/docs/tables-intro#standard-tables). You can bring external data into BigQuery, or replicate it locally with materialized view replicas to help you join datasets across Google Cloud regions and use [BigQuery ML](https://docs.cloud.google.com/bigquery/docs/bqml-introduction). To transfer large files from Amazon S3 or Blob Storage on a recurring schedule, use [BigQuery Data Transfer Service](https://docs.cloud.google.com/bigquery/docs/dts-introduction) instead. ## Supported transfer methods You can transfer data into BigQuery in the following ways: - Transfer data from files in Amazon S3 and Azure Blob Storage into BigQuery tables, by using the [`LOAD DATA` statement](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#load-data). - Filter data from files in Amazon S3 or Blob Storage before transferring results into BigQuery tables, by using the [`CREATE TABLE AS SELECT` statement](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#filter-data). To append data to the destination table, use the [`INSERT INTO SELECT` statement](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#filter-data). Data manipulation is applied on the external tables that reference data from [Amazon S3](https://docs.cloud.google.com/bigquery/docs/omni-aws-create-external-table) or [Blob Storage](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-external-table). - Create [materialized view replicas](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#materialized_view_replicas) of external Amazon S3, Apache Iceberg, or Salesforce Data 360 data in a BigQuery dataset so that the data is available locally in BigQuery. > [!NOTE] > **Note:** If you want to transfer large files from Amazon Simple Storage Service (Amazon S3) bucket or Azure Blob Storage into BigQuery tables on a scheduled basis, use [BigQuery Data Transfer Service](https://docs.cloud.google.com/bigquery/docs/dts-introduction). If you want to read and process data before transferring data into BigQuery tables, use the [`CREATE TABLE AS SELECT` statement](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#filter-data). ## Quotas and limits For information about quotas and limits, see [query jobs quotas and limits](https://docs.cloud.google.com/bigquery/quotas#query_jobs). ## Before you begin To provide Google Cloud with read access required to load or filter data in other clouds, ask your administrator to create a [connection](https://docs.cloud.google.com/bigquery/docs/connections-api-intro) and share it with you. For information about how to create connections, see [Connect to Amazon S3](https://docs.cloud.google.com/bigquery/docs/omni-aws-create-connection) or [Blob Storage](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-connection). ### Required role To get the permissions that you need to load data using BigQuery Omni transfers, ask your administrator to grant you the [BigQuery Data Editor](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery#bigquery.dataEditor) (`roles/bigquery.dataEditor`) IAM role on the dataset. For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access). This predefined role contains the permissions required to load data using BigQuery Omni transfers. To see the exact permissions that are required, expand the **Required permissions** section: #### Required permissions The following permissions are required to load data using BigQuery Omni transfers: - `bigquery.tables.create` - `bigquery.tables.get` - `bigquery.tables.updateData` - `bigquery.tables.update` - `bigquery.jobs.create` - `bigquery.connections.use` You might also be able to get these permissions with [custom roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles) or other [predefined roles](https://docs.cloud.google.com/iam/docs/roles-overview#predefined). For more information about IAM roles in BigQuery, see [BigQuery IAM roles and permissions](https://docs.cloud.google.com/bigquery/docs/access-control). ## Pricing You are billed for the bytes that are transferred across clouds by using the [`LOAD` statement](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#load-data). For pricing information, see the Omni Data Transfer section in [BigQuery Omni pricing](https://cloud.google.com/bigquery/pricing#bqomni). You are billed for the bytes that are transferred across clouds by using the [`CREATE TABLE AS SELECT` statement](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#filter-data) or [`INSERT INTO SELECT` statement](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#filter-data) and for the [compute capacity](https://cloud.google.com/bigquery/pricing#capacity_compute_analysis_pricing). Both `LOAD` and `CREATE TABLE AS SELECT` statements require slots in the BigQuery Omni regions to scan Amazon S3 and Blob Storage files to load them. For more information, see [BigQuery Omni pricing](https://cloud.google.com/bigquery/pricing#bqomni). For materialized view replicas of external data sources, costs can also include [materialized views pricing](https://docs.cloud.google.com/bigquery/docs/materialized-views-intro#materialized_views_pricing). ## Best practices for load and filter options - Avoid loading multiple files that are less than 5 MB. Instead, create an external table for your file and export query result to [Amazon S3](https://docs.cloud.google.com/bigquery/docs/omni-aws-create-external-table) or [Blob Storage](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-external-table) to create a larger file. This method helps to improve the transfer time of your data. For information about the limit for maximum query result, see [BigQuery Omni maximum query result size](https://docs.cloud.google.com/bigquery/quotas#max_result_size_query_omni). - If your source data is in a gzip-compressed file, then while creating external tables, set the [`external_table_options.compression`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#external_table_option_list) option to `GZIP`. ## Load data You can load data into BigQuery with the [`LOAD DATA [INTO|OVERWRITE]` statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/load-statements). ### Limitations - The connection and the destination dataset must belong to the same project. Loading data across projects is not supported. - `LOAD DATA` is only supported when you transfer data from an Amazon Simple Storage Service (Amazon S3) or Azure Blob Storage to a colocated BigQuery region. For more information, see [Locations](https://docs.cloud.google.com/bigquery/docs/omni-introduction#locations). - You can transfer data from any `US` region to a `US` multi-region. You can also transfer from any `EU` region to a `EU` multi-region. ### Example #### Example 1 The following example loads a parquet file named `sample.parquet` from an Amazon S3 bucket into the `test_parquet` table with an auto-detect schema: ```googlesql LOAD DATA INTO mydataset.testparquet FROM FILES ( uris = ['s3://test-bucket/sample.parquet'], format = 'PARQUET' ) WITH CONNECTION `aws-us-east-1.test-connection` ``` #### Example 2 The following example loads a CSV file with the prefix `sampled*` from your Blob Storage into the `test_csv` table with predefined column partitioning by time: ```googlesql LOAD DATA INTO mydataset.test_csv (Number INT64, Name STRING, Time DATE) PARTITION BY Time FROM FILES ( format = 'CSV', uris = ['azure://test.blob.core.windows.net/container/sampled*'], skip_leading_rows=1 ) WITH CONNECTION `azure-eastus2.test-connection` ``` #### Example 3 The following example overwrites the existing table `test_parquet` with data from a file named `sample.parquet` with an auto-detect schema: ```googlesql LOAD DATA OVERWRITE mydataset.testparquet FROM FILES ( uris = ['s3://test-bucket/sample.parquet'], format = 'PARQUET' ) WITH CONNECTION `aws-us-east-1.test-connection` ``` ## Filter data You can filter data before transferring them into BigQuery by using the [`CREATE TABLE AS SELECT` statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_table_statement) and the [`INSERT INTO SELECT` statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/dml-syntax#insert_statement). ### Limitations - If the result of the `SELECT` query exceeds 60 GiB in logical bytes, the query fails. The table is not created and data is not transferred. To learn how to reduce the size of data that is scanned, see [Reduce data processed in queries](https://docs.cloud.google.com/bigquery/docs/best-practices-performance-communication). - Temporary tables are not supported. - Transferring the [Well-known binary (WKB)](https://docs.cloud.google.com/bigquery/docs/geospatial-data) geospatial data format is not supported. - `INSERT INTO SELECT` statement does not support transferring data into clustered table. - In the `INSERT INTO SELECT` statement, if the destination table is the same as the source table in the `SELECT` query, then the `INSERT INTO SELECT` statement doesn't modify any rows in the destination table. The destination table isn't modified as BigQuery can't read data across regions. - `CREATE TABLE AS SELECT` and `INSERT INTO SELECT` are only supported when you transfer data from an Amazon S3 or Blob Storage to a colocated BigQuery region. For more information, see [Locations](https://docs.cloud.google.com/bigquery/docs/omni-introduction#locations). - You can transfer data from any `US` region to a `US` multi-region. You can also transfer from any `EU` region to a `EU` multi-region. ### Example #### Example 1 Suppose you have a BigLake table named `myawsdataset.orders` that references data from [Amazon S3](https://docs.cloud.google.com/bigquery/docs/omni-aws-create-external-table). You want to transfer data from that table to a BigQuery table `myotherdataset.shipments` in the US multi-region. First, display information about the `myawsdataset.orders` table: ```bash bq show myawsdataset.orders; ``` The output is similar to the following: ``` Last modified Schema Type Total URIs Expiration --- --- --- --- --- 31 Oct 17:40:28 |- l_orderkey: integer EXTERNAL 1 |- l_partkey: integer |- l_suppkey: integer |- l_linenumber: integer |- l_returnflag: string |- l_linestatus: string |- l_commitdate: date ``` Next, display information about the `myotherdataset.shipments` table: ```bash bq show myotherdataset.shipments ``` The output is similar to the following. Some columns are omitted to simplify the output. ``` Last modified Schema Total Rows Total Bytes Expiration Time Partitioning Clustered Fields Total Logical --- --- --- --- --- --- --- --- 31 Oct 17:34:31 |- l_orderkey: integer 3086653 210767042 210767042 |- l_partkey: integer |- l_suppkey: integer |- l_commitdate: date |- l_shipdate: date |- l_receiptdate: date |- l_shipinstruct: string |- l_shipmode: string ``` Now, using the `CREATE TABLE AS SELECT` statement you can selectively load data to the `myotherdataset.orders` table in the US multi-region: ```googlesql CREATE OR REPLACE TABLE myotherdataset.orders PARTITION BY DATE_TRUNC(l_commitdate, YEAR) AS SELECT * FROM myawsdataset.orders WHERE EXTRACT(YEAR FROM l_commitdate) = 1992; ``` > [!NOTE] > **Note:** If you get a `ResourceExhausted` error, retry after some time. If the issue persists, you can [contact support](https://docs.cloud.google.com/bigquery/docs/getting-support). You can then perform a join operation with the newly created table: ```googlesql SELECT orders.l_orderkey, orders.l_orderkey, orders.l_suppkey, orders.l_commitdate, orders.l_returnflag, shipments.l_shipmode, shipments.l_shipinstruct FROM myotherdataset.shipments JOIN `myotherdataset.orders` as orders ON orders.l_orderkey = shipments.l_orderkey AND orders.l_partkey = shipments.l_partkey AND orders.l_suppkey = shipments.l_suppkey WHERE orders.l_returnflag = 'R'; -- 'R' means refunded. ``` When new data is available, append the data of the 1993 year to the destination table using the `INSERT INTO SELECT` statement: ```googlesql INSERT INTO myotherdataset.orders SELECT * FROM myawsdataset.orders WHERE EXTRACT(YEAR FROM l_commitdate) = 1993; ``` #### Example 2 The following example inserts data into an ingestion-time partitioned table: ```googlesql CREATE TABLE mydataset.orders(id String, numeric_id INT64) PARTITION BY _PARTITIONDATE; ``` After creating a partitioned table, you can insert data into the ingestion-time partitioned table: ```googlesql INSERT INTO mydataset.orders( _PARTITIONTIME, id, numeric_id) SELECT TIMESTAMP("2023-01-01"), id, numeric_id, FROM mydataset.ordersof23 WHERE numeric_id > 4000000; ``` ## Materialized view replicas A materialized view replica is a replication of external Amazon Simple Storage Service (Amazon S3), Apache Iceberg, or Salesforce Data 360 data in a BigQuery dataset so that the data is available locally in BigQuery. This can help you avoid data egress costs and improve query performance. BigQuery lets you [create materialized views on BigLake metadata cache-enabled tables](https://docs.cloud.google.com/bigquery/docs/omni-introduction#cache-enabled_tables_with_materialized_views) over Amazon Simple Storage Service (Amazon S3), Apache Iceberg, or Salesforce Data 360 data. A materialized view replica lets you use the Amazon S3, Iceberg, or Data 360 materialized view data in queries while avoiding data egress costs and improving query performance. A materialized view replica does this by replicating the Amazon S3, Iceberg, or Data 360 data to a dataset in a [supported BigQuery region](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#supported_regions), so that the data is available locally in BigQuery. ### Before you begin 1. Ensure that you have the [required Identity and Access Management (IAM) permissions](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#required_permissions) to perform the tasks in this section.
#### Required roles To get the permissions that you need to perform the tasks in this section, ask your administrator to grant you the [BigQuery Admin](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery#bigquery.admin) (`roles/bigquery.admin`) IAM role. For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access). This predefined role contains the permissions required to perform the tasks in this section. To see the exact permissions that are required, expand the **Required permissions** section: #### Required permissions The following permissions are required to perform the tasks in this section: - `bigquery.tables.create` - `bigquery.tables.get` - `bigquery.tables.getData` - `bigquery.tables.replicateData` - `bigquery.jobs.create` You might also be able to get these permissions with [custom roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles) or other [predefined roles](https://docs.cloud.google.com/iam/docs/roles-overview#predefined). For more information about BigQuery IAM, see [BigQuery IAM roles and permissions](https://docs.cloud.google.com/bigquery/docs/access-control). ### Prepare a dataset for materialized view replicas Before creating a materialized view replica, you must complete the following tasks: 1. [Create a dataset](https://docs.cloud.google.com/bigquery/docs/datasets) in a [region that supports Amazon S3](https://docs.cloud.google.com/bigquery/docs/omni-introduction#locations) 2. Create a source table in the dataset you created in the preceding step. The source table can be any of the following table types: - An [Amazon S3 BigLake table](https://docs.cloud.google.com/bigquery/docs/omni-aws-create-external-table) that has [metadata caching](https://docs.cloud.google.com/bigquery/docs/metadata-caching) enabled and doesn't use an Iceberg file format. - An [Apache Iceberg external table](https://docs.cloud.google.com/bigquery/docs/iceberg-external-tables). - A [Data 360 table](https://docs.cloud.google.com/bigquery/docs/salesforce-quickstart). ### Create materialized view replicas Select one of the following options: ### Console 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the left pane, click **Explorer**: ![Highlighted button for the Explorer pane.](https://docs.cloud.google.com/static/bigquery/images/explorer-tab.png) If you don't see the left pane, click **Expand left pane** to open the pane. 3. In the **Explorer** pane, navigate to the project and dataset where you want to create the materialized view replica, and then click **Actions \> Create table**. 4. In the **Source** section of the **Create table** dialog, do the following: 1. For **Create table from** , select **Existing table/view**. 2. For **Project**, enter the project where the source table or view is located. 3. For **Dataset**, enter the dataset where the source table or view is located. 4. For **View** , enter the source table or view that you are replicating. If you choose a view, it must be an [authorized view](https://docs.cloud.google.com/bigquery/docs/authorized-views), or if not, all tables that are used to generate that view must be located in the view's dataset. 5. Optional: For **Local materialized view max staleness** , enter a [`max_staleness` value](https://docs.cloud.google.com/bigquery/docs/materialized-views-create#max_staleness) for your local materialized view. 6. In the **Destination** section of the **Create table** dialog, do the following: 1. For **Project**, enter the project in which you want to create the materialized view replica. 2. For **Dataset**, enter the dataset in which you want to create the materialized view replica. 3. For **Replica materialized view name**, enter a name for your replica. 7. Optional: Specify **tags** and **advanced options** for your materialized view replica. If you don't specify a dataset for **Local Materialized View Dataset** , then one is automatically created in the same project and region as the source data and named `bq_auto_generated_local_mv_dataset`. If you don't specify a name for **Local Materialized View Name** , then one is automatically created in the same project and region as the source data and given the prefix `bq_auto_generated_local_mv_`. 8. Click **Create table**. A new local materialized view is created (if it wasn't specified) and authorized in the source dataset. Then the materialized view replica is created in the destination dataset. ### SQL 1. [Create a materialized view](https://docs.cloud.google.com/bigquery/docs/materialized-views-create) over the base table in the dataset that you created. You can also create the materialized view in a different dataset that is in an Amazon S3 region. 2. [Authorize the materialized view](https://docs.cloud.google.com/bigquery/docs/authorized-views) on the datasets that contain the source tables used in the query that created the materialized view. 3. If you configured manual metadata cache refreshing for the source table, run the [`BQ.REFRESH_EXTERNAL_METADATA_CACHE` system procedure](https://docs.cloud.google.com/bigquery/docs/reference/system-procedures#bqrefresh_external_metadata_cache) to refresh the metadata cache. 4. Run the [`BQ.REFRESH_MATERIALIZED_VIEW` system procedure](https://docs.cloud.google.com/bigquery/docs/reference/system-procedures#bqrefresh_materialized_view) to refresh the materialized view. 5. Create materialized view replicas by using the [`CREATE MATERIALIZED VIEW AS REPLICA OF` statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_materialized_view_as_replica_of_statement): ```googlesql CREATE MATERIALIZED VIEW PROJECT_ID.BQ_DATASET.REPLICA_NAME OPTIONS(replication_interval_seconds=REPLICATION_INTERVAL) AS REPLICA OF PROJECT_ID.S3_DATASET.MATERIALIZED_VIEW_NAME; ``` Replace the following: - `PROJECT_ID`: the name of your project in which you want to create the materialized view replica---for example, `myproject`. - `BQ_DATASET`: the name of the BigQuery dataset that you want to create the materialized view replica in---for example, `bq_dataset`. The dataset must be in the BigQuery [region](https://docs.cloud.google.com/bigquery/docs/omni-introduction#locations) that maps to the region of the source materialized view. - `REPLICA_NAME`: the name of the materialized view replica that you want to create---for example, `my_mv_replica`. - `REPLICATION_INTERVAL`: specifies how often to replicate the data from the source materialized view to the replica, in seconds. Must be a value between 60 and 3,600, inclusive. Defaults to 300 (5 minutes). - `S3_DATASET`: the name of the dataset that contains the source materialized view---for example, `s3_dataset`. - `MATERIALIZED_VIEW_NAME`: the name of the materialized view to replicate---for example, `my_mv`.
The following example creates a materialized view replica named `mv_replica` in `bq_dataset`: ```googlesql CREATE MATERIALIZED VIEW `myproject.bq_dataset.mv_replica` OPTIONS( replication_interval_seconds=600 ) AS REPLICA OF `myproject.s3_dataset.my_s3_mv` ``` After you create the materialized view replica, the replication process polls the source materialized view for changes and replicates data to the materialized view replica, refreshing the data at the interval you specified in the `replication_interval_seconds` or `max_staleness` option. If you query the replica before the first backfill completes, you get a `backfill in progress` error. You can query the data in the materialized view replica after the first replication completes. ### Data freshness After you create the materialized view replica, the replication process polls the source materialized view for changes and replicates data to the materialized view replica. The data is replicated at the interval you specified in the `replication_interval_seconds` option of the [`CREATE MATERIALIZED VIEW AS REPLICA OF` statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_materialized_view_as_replica_of_statement). In addition to the replication interval, the freshness of the materialized view replica data is also affected by how often the source materialized view refreshes, and how often the metadata cache of the Amazon S3, Iceberg, or Data 360 table used by the materialized view refreshes. You can check the data freshness for the materialized view replica and the resources it is based on by using the Google Cloud console: - For materialized view replica freshness, look at the **Last modified** field in the materialized view replica's **Details** pane. - For source materialized view freshness, look at the **Last modified** field in the materialized view's **Details** pane. - For source Amazon S3, Iceberg, or Data 360 table metadata cache freshness, look at the **Max staleness** field in the materialized view's **Details** pane. ### Supported materialized view replica regions Use the location mappings in the following table when creating materialized view replicas: | **Location of the source materialized view** | **Location of the materialized view replica** | |---|---| | `aws-us-east-1` | The `US` [multi-region](https://docs.cloud.google.com/bigquery/docs/locations#multi-regions), or any of the following [regions](https://docs.cloud.google.com/bigquery/docs/locations#regions): - `northamerica-northeast1` - `northamerica-northeast2` - `us-central1` - `us-east1` - `us-east4` - `us-east5` - `us-south1` - `us-west1` - `us-west2` - `us-west3` - `us-west4` | | `aws-us-west-2` | The `US` [multi-region](https://docs.cloud.google.com/bigquery/docs/locations#multi-regions), or any of the following [regions](https://docs.cloud.google.com/bigquery/docs/locations#regions): - `northamerica-northeast1` - `northamerica-northeast2` - `us-central1` - `us-east1` - `us-east4` - `us-east5` - `us-south1` - `us-west1` - `us-west2` - `us-west3` - `us-west4` | | `aws-eu-west-1` | The `EU` [multi-region](https://docs.cloud.google.com/bigquery/docs/locations#multi-regions), or any of the following [regions](https://docs.cloud.google.com/bigquery/docs/locations#regions): - `europe-central2` - `europe-north1` - `europe-southwest1` - `europe-west1` - `europe-west2` - `europe-west3` - `europe-west4` - `europe-west6` - `europe-west8` - `europe-west9` - `europe-west10` | | `aws-ap-northeast-2` | Any of the following [regions](https://docs.cloud.google.com/bigquery/docs/locations#regions): - `asia-east1` - `asia-east2` - `asia-northeast1` - `asia-northeast2` - `asia-northeast3` - `asia-south1` - `asia-south2` - `asia-southeast1` | | `aws-ap-southeast-2` | Any of the following [regions](https://docs.cloud.google.com/bigquery/docs/locations#regions): - `australia-southeast1` - `australia-southeast2` | ### Limitations of materialized view replicas - You can't create materialized view replicas for materialized views that are based on any tables that use [row-level security](https://docs.cloud.google.com/bigquery/docs/row-level-security-intro) or [column-level security](https://docs.cloud.google.com/bigquery/docs/column-level-security-intro). - You can't use [customer-managed encryption keys (CMEKs)](https://docs.cloud.google.com/bigquery/docs/customer-managed-encryption) with either the source materialized view or the materialized view replica. - You can only create materialized view replicas for materialized views that are based on any tables that use [metadata caching](https://docs.cloud.google.com/bigquery/docs/metadata-caching). - You can create only one materialized view replica for a given source materialized view. - You can only create materialized view replicas for [authorized materialized views](https://docs.cloud.google.com/bigquery/docs/authorized-views). ### Materialized view replica pricing Use of materialized view replicas incurs compute, outbound data transfer, and storage costs. ## What's next - Learn about [BigQuery ML](https://docs.cloud.google.com/bigquery/docs/bqml-introduction). - Learn about [BigQuery Omni](https://docs.cloud.google.com/bigquery/docs/omni-introduction). - Learn how to [run queries](https://docs.cloud.google.com/bigquery/docs/running-queries). - Learn how to [set up VPC Service Controls for BigQuery Omni](https://docs.cloud.google.com/bigquery/docs/omni-vpc-sc). - Learn how to schedule and manage recurring load jobs from [Amazon S3 into BigQuery](https://docs.cloud.google.com/bigquery/docs/s3-transfer-intro) and [Blob Storage into BigQuery](https://docs.cloud.google.com/bigquery/docs/blob-storage-transfer-intro).