An enterprise collects high-volume telemetry and application logs published to a Google Cloud Pub/Sub topic. The engineering team needs to ingest these messages in real time into BigQuery for immediate SQL analytics while minimizing operational overhead and avoiding the deployment or maintenance of intermediate data processing pipelines. Which ingestion solution should the team implement?

Create a Pub/Sub BigQuery subscription configured to stream incoming messages directly into the destination BigQuery table.: ### **What a Pub/Sub BigQuery Subscription is** A **Pub/Sub BigQuery subscription** is a specialized, fully managed subscription type in Google Cloud Pub/Sub that automatically writes messages directly to an existing **BigQuery table** as they are received, completely eliminating the need to write subscriber code or deploy an intermediary pipeline. ### **How it meets the requirements** - **Eliminates intermediate pipelines (✓):** Traditional architectures require running a stream processing framework (such as Apache Beam on Dataflow or custom consumer services) to pull messages from Pub/Sub and insert them into BigQuery. A BigQuery subscription is direct and serverless. - **Minimizes operational overhead (✓):** Google Cloud handles all message consumption, scaling, retries, and data ingestion behind the scenes. There are no virtual machines, worker clusters, or streaming jobs to monitor and patch. - **Real-time availability (✓):** Messages are streamed directly to BigQuery tables upon arrival, making data immediately accessible for SQL queries, reporting, and operational monitoring. - **Schema alignment (✓):** BigQuery subscriptions support schema mapping (matching topic schemas or JSON attributes to BigQuery columns) as well as writing message metadata (such as publish timestamp and message ID) directly into table fields. ### **Key advantages and capabilities** - **Serverless and fully managed:** Scales automatically with message throughput without manual capacity planning. - **Direct ingestion path:** Minimizes latency between event generation (e.g., IoT telemetry, app logs) and analytics. - **Dead-letter topic support:** Failed records (such as schema mismatches) can be routed to a dead-letter topic for debugging. - **Cost efficiency:** Removes the compute and licensing costs associated with running continuous intermediate processing engines. ### **Why this is optimal** When messages already conform to the destination BigQuery table schema (or when simple ingestion of raw payloads and metadata is sufficient), using a **BigQuery subscription** provides the simplest, lowest-latency, and most cost-effective architecture available in Google Cloud.