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Answer: Choose BigQuery as your data warehouse solution, configuring output destinations for caching extensive queries.
Given the requirement to handle large datasets efficiently and cost-effectively, BigQuery emerges as the optimal choice. It is a fully managed, serverless data warehouse capable of storing and querying vast amounts of data swiftly. BigQuery's SQL-based access facilitates easy data retrieval for analysts and data scientists. Additionally, setting up output destinations for caching large queries can enhance performance and reduce costs by minimizing redundant queries. While Cloud SQL (Option C) may not scale well for such large databases, and a MySQL cluster (Option A) demands more maintenance, Cloud Spanner (Option B) might not be cost-justifiable for this scenario unless high scalability and strict consistency are paramount.
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You are tasked with finding a cost-effective solution to retrieve large datasets of medical information from a database exceeding 10 TBs. The solution should allow for storing this data in new tables for further querying, feature a low-maintenance architecture, and be accessible via SQL. Which option would you choose?
A
Deploy a MySQL cluster on a Compute Engine managed instance group to ensure scalability.
B
Opt for Cloud Spanner to replicate data across regions and organize it into normalized tables.
C
Utilize Cloud SQL, structure your data into tables, and employ JOIN operations in your queries for data retrieval.
D
Choose BigQuery as your data warehouse solution, configuring output destinations for caching extensive queries.