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Answer: Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
The correct answer is B. Transforming text files to compressed Avro using Cloud Dataflow and using Cloud Storage with BigQuery permanent linked tables meets all the criteria of supporting ANSI SQL queries, compression, and parallel load. This approach allows efficient storage and querying, taking advantage of Avro's compression capabilities and parallel processing capabilities supported by Cloud Dataflow. Additionally, using Cloud Storage with BigQuery linked tables avoids the additional storage costs and import overhead associated with directly loading large amounts of data into BigQuery.
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As a data engineer tasked with designing storage for a data pipeline on Google Cloud, you must handle very large text files. Your design needs to support ANSI SQL queries and enable efficient compression. Furthermore, it is crucial to support parallel loading from the input locations while adhering to Google's recommended best practices. What steps should you take to achieve this?
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.
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