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An e-commerce enterprise generates several terabytes of transactional sales data daily. The data analytics team needs to execute complex SQL queries and aggregate calculations across specific data columns to generate daily business intelligence reports.
The workload has the following requirements:
Which storage and analytics architecture should you recommend?
Store raw transaction files exclusively in Cloud Storage Standard, create BigQuery external tables to query the data in place, and apply bucket lifecycle rules to transition objects to Nearline storage after 90 days.
Load data into date-partitioned BigQuery tables for columnar SQL analytics, utilize BigQuery's automatic transition to long-term storage pricing for unmodified partitions older than 90 days, and export compliance backups to Cloud Storage Coldline with Object Lifecycle Management.
Deploy a Cloud SQL for PostgreSQL database instance with regional Persistent Disk SSD storage, perform analytical queries directly in the database, and configure scheduled SQL scripts to archive older tables to Filestore.
Ingest transactional records into Cloud Bigtable with single-row key indexing, execute analytical SQL aggregation jobs via Bigtable compute nodes, and configure table garbage collection policies to export historical rows to Cloud Storage Standard.
BigQuery is an enterprise, serverless cloud data warehouse built for large-scale analytics, utilizing a proprietary columnar storage format (Capacitor) and a distributed query execution engine (Dremel). Cloud Storage provides scalable, highly durable object storage with distinct storage classes—such as Coldline and Archive—governed by automated Object Lifecycle Management policies.
This architecture directly addresses high-throughput columnar analytics while fully utilizing native Google Cloud billing models and storage lifecycle rules, eliminating manual maintenance scripts and compute provisioning.
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