An enterprise runs an operational and analytics data platform on Google Cloud with the following access profiles and requirements: IoT operational telemetry: High-throughput time-series data is written directly to Cloud Bigtable. Real-time dashboards require single-digit millisecond latency for the most recent 30 days of data. Records between 31 and 365 days are queried infrequently for troubleshooting. Analytical summary data: Historical reporting tables in BigQuery are queried for business analysis. Once written, partitions or tables older than 90 days are never updated or modified, but analysts require native query performance without latency degradation. Raw telemetry archives: Raw event payload objects in Cloud Storage are stored for disaster recovery and operational audits, with expected read access occurring roughly once every 45 to 60 days. Which multi-service tiered storage strategy optimizes storage and retrieval costs while maintaining the required query latency?

Configure Bigtable tiered storage with a 30-day rolling age threshold and apply timestamp range filters on real-time queries; rely on BigQuery automatic long-term storage pricing for unmodified tables older than 90 days; and transition Cloud Storage raw objects to Nearline storage via Object Lifecycle Management.: ### What the Multi-Service Tiered Architecture Is This architecture optimizes cost across disparate Google Cloud storage systems (**Cloud Bigtable**, **BigQuery**, and **Cloud Storage**) by mapping specific data lifecycle policies and storage classes to the distinct access frequency, query latency, and retention requirements of each workload. ### How It Meets Every Requirement - **Cloud Bigtable Cost and Performance**: Enabling tiered storage with a 30-day rolling age threshold maintains the hot 30 days of telemetry in high-performance **SSD storage** while automatically migrating data older than 30 days to the lower-cost **infrequent access tier**. Applying a **timestamp range filter** to operational queries limits reads strictly to the SSD tier, preventing performance degradation caused by checking both storage tiers. - **BigQuery Long-Term Storage**: BigQuery automatically reduces storage pricing by 50% for any table or partition that has not been modified for 90 consecutive days. This provides substantial cost savings without requiring export jobs, schema changes, or query latency trade-offs. - **Cloud Storage Nearline Class**: The **Nearline** storage class is engineered for data accessed at most once a month, featuring a **30-day minimum storage duration**. It provides lower monthly storage rates than Standard storage while keeping retrieval fees cost-effective for 45–60 day access cycles. ### Key Advantages - **Zero Latency Penalty**: BigQuery long-term storage and Bigtable SSD-filtered reads maintain baseline query latencies. - **Automated Lifecycle Transitions**: Storage tiering occurs seamlessly via native background compaction and automated pricing transitions without manual intervention. - **Cost Elimination**: Avoids minimum storage duration penalties in Cloud Storage and reduces Bigtable SSD footprint. ### Why This Is Optimal This solution strikes the exact balance between storage-class rates, retrieval fees, and query latency across all three specialized GCP services.

Multi Service Tiered Storage Cost Optimization | professional-cloud-data-engineer

Multi Service Tiered Storage Cost Optimization

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An enterprise runs an operational and analytics data platform on Google Cloud with the following access profiles and requirements:

IoT operational telemetry: High-throughput time-series data is written directly to Cloud Bigtable. Real-time dashboards require single-digit millisecond latency for the most recent 30 days of data. Records between 31 and 365 days are queried infrequently for troubleshooting.
Analytical summary data: Historical reporting tables in BigQuery are queried for business analysis. Once written, partitions or tables older than 90 days are never updated or modified, but analysts require native query performance without latency degradation.
Raw telemetry archives: Raw event payload objects in Cloud Storage are stored for disaster recovery and operational audits, with expected read access occurring roughly once every 45 to 60 days.

Which multi-service tiered storage strategy optimizes storage and retrieval costs while maintaining the required query latency?

Set a Bigtable garbage collection policy to permanently delete rows older than 30 days; configure BigQuery table expiration to 90 days for all analytical tables; and retain Cloud Storage raw event payloads in Standard storage indefinitely.

Configure Bigtable tiered storage with a 30-day age threshold without timestamp filters; export BigQuery tables older than 90 days to Coldline Cloud Storage and query them using external tables; and transition raw payload objects to Archive storage immediately.

Migrate the entire Bigtable cluster storage type from SSD to HDD; schedule monthly scheduled queries in BigQuery to truncate and re-insert 90-day-old data into partitioned datasets; and configure Cloud Storage lifecycle rules to move raw objects to Coldline storage after 30 days.

Configure Bigtable tiered storage with a 30-day rolling age threshold and apply timestamp range filters on real-time queries; rely on BigQuery automatic long-term storage pricing for unmodified tables older than 90 days; and transition Cloud Storage raw objects to Nearline storage via Object Lifecycle Management.

Professional Cloud Data Engineer

Multi Service Tiered Storage Cost Optimization

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