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A data architect is configuring an automated pipeline to ingest daily e-commerce order records stored as newline-delimited JSON files in Cloud Storage into BigQuery. The implementation must meet the following operational requirements:
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StudyCertsretail_dw.orders must be partitioned daily on an existing order_timestamp column.WHERE clause to avoid costly accidental full-table scans.RECORD fields.Which bq command-line invocation correctly configures the table parameters and loads the files?
Execute bq load --source_format=NEWLINE_DELIMITED_JSON --time_partitioning_field order_timestamp --time_partitioning_type DAY --time_partitioning_expiration 7776000 --require_partition_filter retail_dw.orders gs://retail-bucket/orders/*.json ./order_schema.json
Execute bq load --source_format=NEWLINE_DELIMITED_JSON --autodetect --time_partitioning_type DAY --time_partitioning_expiration 7776000 retail_dw.orders gs://retail-bucket/orders/*.json
Execute bq load --source_format=NEWLINE_DELIMITED_JSON --time_partitioning_field order_timestamp --schema order_id:STRING,amount:FLOAT,order_timestamp:TIMESTAMP retail_dw.orders gs://retail-bucket/orders/*.json
Execute bq mk --table --schema=./order_schema.json retail_dw.orders and then run gcloud storage cp gs://retail-bucket/orders/*.json bq://retail_dw.orders
Execute bq load --source_format=NEWLINE_DELIMITED_JSON --time_partitioning_field order_timestamp --time_partitioning_type DAY --time_partitioning_expiration 7776000 --require_partition_filter retail_dw.orders gs://retail-bucket/orders/*.json ./order_schema.json
bq load command isThe bq load command in the Google Cloud SDK creates a BigQuery load job that ingests data from external sources (such as Cloud Storage) into a destination table while simultaneously establishing table properties, schemas, and performance configurations.
--time_partitioning_field order_timestamp and --time_partitioning_type DAY configures the destination table to partition data by day according to values in the order_timestamp column rather than table ingestion time.--time_partitioning_expiration 7776000 defines a partition time-to-live of exactly 90 days in seconds, automatically deleting stale partitions and eliminating unnecessary storage charges.--require_partition_filter enforces mandatory partition filtering on all downstream queries, preventing analysts and BI tools from incurring high scanning costs from unintentional full-table scans../order_schema.json schema file allows BigQuery to apply explicit data types, RECORD (STRUCT) elements, and field modes (NULLABLE or REQUIRED).NEWLINE_DELIMITED_JSON formatted files directly from Cloud Storage bucket wildcards.This approach configures all data governance, performance tuning, and lifecycle parameters at the point of ingestion using standard, supported bq CLI flags without requiring multi-step DDL execution or external orchestration tools.
Execute bq load --source_format=NEWLINE_DELIMITED_JSON --autodetect --time_partitioning_type DAY --time_partitioning_expiration 7776000 retail_dw.orders gs://retail-bucket/orders/*.json
Execute bq load --source_format=NEWLINE_DELIMITED_JSON --time_partitioning_field order_timestamp --schema order_id:STRING,amount:FLOAT,order_timestamp:TIMESTAMP retail_dw.orders gs://retail-bucket/orders/*.json
Execute bq mk --table --schema=./order_schema.json retail_dw.orders and then run gcloud storage cp gs://retail-bucket/orders/*.json bq://retail_dw.orders