Your media company streams click-level events from a Pub/Sub topic into a data processing pipeline. Analysts must run SQL-based, ad-hoc queries that join 18 months (≈300 TB) of event data with several dimension tables and feed Looker Studio dashboards that refresh every few minutes. The solution must minimize operational overhead, enforce column-level security, and scale storage and compute independently. Which Google Cloud service should you choose as the primary data sink?
Cloud Storage bucket queried by an on-demand Dataproc Spark cluster
BigQuery is Google Cloud's fully managed, serverless analytical data warehouse designed for petabyte-scale SQL workloads. It supports interactive, ad-hoc queries, automatic storage and compute scaling, built-in column-level security, and near real-time ingestion from streaming pipelines with minimal operational effort.
Cloud Bigtable excels at low-latency key/value access but lacks native SQL joins and aggregation. Cloud Spanner offers horizontal scalability for transactional OLTP workloads rather than large analytic scans. Storing data in Cloud Storage and querying it with on-demand Dataproc clusters would require ongoing cluster management and would not natively provide column-level security or automatic scaling for dashboards, resulting in higher operational overhead and latency.
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Ingesting and processing the data
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