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Zorluk: ZorSelecting and Designing Storage and Database Resources

Match each workload data requirement and access pattern on the left with its corresponding optimal managed Google Cloud storage or database service architecture on the right.

  • High-throughput time-series IoT metrics ingestion requiring sustained sub-10ms write latency, seamless scale-out without operational downtime, and native compatibility with the open-source Apache HBase API.Cloud Bigtable
  • Global financial ledger system requiring multi-region active-active transactional consistency, ANSI SQL support, seamless horizontal read/write scaling, and a 99.999% availability SLA.Cloud Spanner
  • Serverless enterprise data warehouse executing ad-hoc analytics over petabytes of structured data, leveraging decoupled compute and columnar storage with automatic query slot management.BigQuery
  • Cross-platform mobile collaboration tool requiring document-based hierarchy, real-time client state updates via snapshot listeners, and automatic offline data sync.Firestore

Cevap

High-throughput time-series IoT ingestion pairs with Cloud Bigtable. Global financial ledger with ACID consistency and 99.999% SLA pairs with Cloud Spanner. Serverless enterprise analytics over petabytes of data pairs with BigQuery. Cross-platform mobile app with document storage and real-time offline sync pairs with Firestore.
Each Google Cloud database and storage offering is tailored for specific workload characteristics: Cloud Bigtable handles high-throughput NoSQL time-series data with HBase compatibility; Cloud Spanner handles global multi-region relational OLTP with 99.999% SLA; BigQuery handles serverless petabyte-scale analytical querying (OLAP); and Firestore handles NoSQL document data for mobile applications with real-time sync and offline support.

Adım Adım Çözüm

1
Analyze latency, API compatibility, and write throughput requirements for IoT telemetry.
Identified Cloud Bigtable due to sub-10ms key-value write latency and native HBase API support.
Cloud Bigtable is optimized for heavy, continuous ingestion of time-series data without relational transactional overhead.
2
Evaluate global consistency, transaction model, and SLA constraints for global financial ledger.
Identified Cloud Spanner due to multi-region global ACID transactions, SQL interface, and five-nines availability SLA.
Cloud Spanner uses TrueTime and distributed consensus to achieve global horizontal scaling with strict ACID compliance.
3
Examine analytical workload pattern, storage format, and query execution scale.
Identified BigQuery for petabyte-scale serverless OLAP execution.
BigQuery separates compute and storage, providing columnar execution and slot-based processing for ad-hoc analytical queries.
4
Assess mobile/web application synchronization and document storage requirements.
Identified Firestore for document hierarchy, live query snapshot listeners, and offline mobile synchronization.
Firestore is specifically architected for client-facing applications requiring document-level real-time updates.

Anahtar Kavram

Mapping GCP Storage and Database Services to Application Access Patterns, Consistency Requirements, and SLAs
Tahmini Süre:2m 0s
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Selecting and Designing Storage and Database Resources