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An enterprise data lake ingests several terabytes of transactional logs daily into Cloud Storage before loading them into BigQuery for analytics. The engineering team has established the following operational and cost requirements:
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StudyCertsevent_timestamp) and slicing by merchant identifier (merchant_id).Which architectural strategy should you implement to optimize storage and query costs?
Use Storage Batch Operations to transition Cloud Storage objects directly to Coldline immediately upon arrival. In BigQuery, partition the table by integer range on merchant_id and cluster by event_timestamp, setting require_partition_filter to false.
Configure Cloud Storage lifecycle rules to transition objects to Nearline at 30 days and Archive at 90 days. In BigQuery, create a table partitioned by day on event_timestamp, clustered by merchant_id, and configure the require_partition_filter option to true.
Configure Cloud Storage lifecycle rules to transition objects to Nearline after 90 days. In BigQuery, create an ingestion-time partitioned table with BI Engine reservations, and enforce the maximum_bytes_billed limit across all user sessions.
Configure Cloud Storage lifecycle rules to delete raw files after 30 days and rely on BigQuery Time Travel for historical compliance. In BigQuery, create an unpartitioned table with search indexes and set project-level daily query quotas.
Use Storage Batch Operations to transition Cloud Storage objects directly to Coldline immediately upon arrival. In BigQuery, partition the table by integer range on merchant_id and cluster by event_timestamp, setting require_partition_filter to false.
Configure Cloud Storage lifecycle rules to transition objects to Nearline at 30 days and Archive at 90 days. In BigQuery, create a table partitioned by day on event_timestamp, clustered by merchant_id, and configure the require_partition_filter option to true.
This solution combines Cloud Storage Object Lifecycle Management for automated storage class tiering with BigQuery table partitioning, clustering, and partition filter enforcement to minimize both storage and query compute expenditures across data lake environments.
event_timestamp prunes scanned partitions to specific days, while clustering by merchant_id colocates related records within those partitions, significantly reducing scanned byte volume.require_partition_filter = true enforces a mandatory predicate on the partition column at the table level, causing any ad-hoc query without a partition filter to fail immediately before scanning data.This design precisely aligns each storage lifecycle stage to access frequency thresholds while using native BigQuery features to minimize byte scans and enforce query governance directly at the storage schema boundary.
Configure Cloud Storage lifecycle rules to transition objects to Nearline after 90 days. In BigQuery, create an ingestion-time partitioned table with BI Engine reservations, and enforce the maximum_bytes_billed limit across all user sessions.
Configure Cloud Storage lifecycle rules to delete raw files after 30 days and rely on BigQuery Time Travel for historical compliance. In BigQuery, create an unpartitioned table with search indexes and set project-level daily query quotas.