While on-premise, an enterprise had multiple teams, each with its own analytics data store. Attempts to converge the storage for centralized, company-wide analysis failed because of speed and…

Question

Options

• AMigrate to Bigtable which provides high throughput reads and writes.
• BMigrate to Cloud Spanner as a globally scalable SQL database.
• CMigrate to BigQuery as a central data warehouse.
• DMigrate to Cloud SQL which supports multiple databases like MySQL, PostgreSQL, and SQL

• A
  6% (2)
• B
  3% (1)
• C
  82% (27)
• D
  9% (3)

Explanation

BigQuery is Google Cloud's fully managed, serverless data warehouse designed for large-scale analytics. It handles petabyte-scale data with fast SQL queries using massive parallel processing. When multiple teams had siloed analytics stores and centralization failed due to speed and scaling issues, BigQuery solves both problems: it scales automatically without infrastructure management and separates storage from compute, enabling concurrent team access without contention. Bigtable (A) is optimized for low-latency NoSQL workloads, not analytical queries. Cloud Spanner (B) is a transactional database, not a warehouse. Cloud SQL (D) is a managed relational DB that would face the same scaling bottlenecks as the on-premise solution.

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