Architect Scalable Multitenant Bigquery Storage Compute

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A SaaS organization is redesigning its analytics platform on Google Cloud to handle a projected tenfold increase in tenant onboarding and raw data volume. Currently, ingestion workloads, storage, and customer-facing analytical queries share resources in a monolithic project, leading to severe compute contention, complex schema maintenance, and quota bottlenecks.

You need to design a scalable, modular architecture that leverages BigQuery's separation of storage and compute to isolate compute resource allocation across tenant tiers while allowing storage and analytical pipelines to scale independently.

Which architectural approach should you recommend?

Establish a centralized data pipeline project that ingests and dual-writes data into dedicated per-tenant BigQuery datasets within a core data storage project, and decouple query execution by creating compute tier projects mapped to BigQuery slot reservations using fair scheduling.

Maintain all data in a single BigQuery dataset in a monolithic project, and create up to 2,500 tenant-specific authorized views clustered on customer IDs while relying entirely on default on-demand query pricing.

Deploy dedicated Compute Engine virtual machines running Apache Spark for each tenant tier, connecting directly to a shared BigQuery dataset containing a single monolithic fact table filtered using row-level security.

Create a separate Google Cloud project per tenant containing both the tenant's Cloud Storage data lake and dedicated BigQuery on-demand compute resources, connected via cross-project VPC networks.

Professional Cloud Data Engineer

Architect Scalable Multitenant Bigquery Storage Compute

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