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Answer: Load data into Google BigQuery
The correct answer is A. Google BigQuery is designed for large-scale data analysis and fits this scenario well. It is a serverless, highly scalable, and low-cost enterprise data warehouse that allows data analysts to run SQL queries directly. Unlike Google Cloud SQL, which may not scale efficiently for multi-petabyte datasets and is not optimized for large-scale analytical queries, BigQuery is specifically built for such requirements. Google Cloud Storage does not offer a SQL interface, and Google Cloud Datastore is a NoSQL database that is not designed for SQL-based analysis. Therefore, considering the need for scalability, SQL interface, and 24/7 availability, Google BigQuery is the optimal choice.
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Your company is in the process of migrating a multi-petabyte data set to the cloud to improve accessibility and analysis capabilities. The data must be available 24 hours a day to meet operational requirements. Your team of business analysts has experience exclusively with using a SQL interface for their analytical tasks. Considering these factors, how should you store the data to optimize it for ease of analysis?
A
Load data into Google BigQuery
B
Insert data into Google Cloud SQL
C
Put flat files into Google Cloud Storage
D
Stream data into Google Cloud Datastore