GCP Professional Data Engineer Practice Question

Your team is designing a data pipeline that will stream click-stream events from Pub/Sub through Dataflow and land the data in a durable store where hundreds of analysts can run interactive ANSI-SQL queries over several years of historical data. The data volume is expected to grow to multiple petabytes, and the business wants to pay primarily for storage and the queries actually executed rather than for reserved compute. Which Google Cloud service should you choose as the sink for this analytical workload?

Cloud Bigtable
BigQuery
Cloud Storage in an archive bucket
Cloud Spanner

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Answer Description

The workload requires an analytics-optimized store that:

ingests streaming data at high throughput,
automatically scales to petabyte-scale storage without manual capacity planning,
supports familiar ANSI-SQL for ad-hoc analysis by many concurrent users, and
charges mainly for stored data and per-query processing instead of provisioned resources.
BigQuery is Google Cloud's fully managed, serverless, columnar data warehouse that offers streaming inserts, separates storage from compute, auto-scales to exabytes, supports standard SQL, and uses on-demand or flex slot pricing so customers pay only for the data scanned by queries and for the storage they keep. Services like Cloud Bigtable or Cloud Spanner are optimized for low-latency key-value or OLTP use cases and require node provisioning, while Cloud Storage lacks native SQL analytics. Therefore, BigQuery is the appropriate sink.

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