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?
A. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
B. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
C. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
D. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
Answer:C
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?
A. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query. B. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query. C. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query. D. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
Explanation: Avro is compressed format and dataflow for parallel pipeline and bigquery for storage.