Google Cloud Professional Data Engineer — Question 18

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

Answer options

• A. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
• B. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
• C. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
• D. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

Correct answer: B

Explanation

The correct answer is B because it combines the use of Cloud Dataflow to transform text files into a compressed format, and utilizes both Cloud Storage and BigQuery permanent linked tables, which are optimal for ANSI SQL queries. Option A incorrectly suggests using only BigQuery for storage, while options C and D do not align with recommended practices for handling large text files with ANSI SQL support.