For very large text files that must support ANSI SQL, compression, and parallel loading per Google best practices, which storage/format pipeline should you use?
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Correct answer: Convert text files to compressed Avro using Cloud Dataflow; store files in Cloud Storage and expose them to BigQuery as permanent external tables..
Why this is the answer
This option aligns with Google best practices for large text files needing ANSI SQL, compression, and parallel loading. Cloud Dataflow efficiently converts text to compressed Avro, a splittable format ideal for parallel processing. Storing these in Cloud Storage is cost-effective, and BigQuery external tables allow querying the data in place using ANSI SQL without ingestion, leveraging BigQuery's powerful query engine. The first incorrect option suggests storing directly in BigQuery, which is less flexible for external tools and might incur higher storage costs for raw data compared to Cloud Storage. The third and fourth options involve gzip, which is not splittable, hindering parallel loading for very large files. Cloud Bigtable (fourth option) is a NoSQL database, not suitable for ANSI SQL queries.
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