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A financial analytics firm is building an event-driven data ingestion pipeline. Whenever transaction batch files are uploaded to a Cloud Storage bucket, the system must immediately trigger an orchestration process that:
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StudyCerts404 Not Found) and retries transient service faults with backoff, while raising unhandled exceptions.The organization requires a fully serverless, low-latency solution that minimizes operational management and isolates orchestration logic from business logic.
Which architecture should the data engineering team implement to meet these requirements?
Deploy a Cloud Run worker pool with minimum instances set to 0, running custom background bash scripts that periodically query the Cloud Storage API and trigger services.
Configure an Application Integration flow using a visual drag-and-drop mapper, setting a Cron schedule trigger to scan the storage bucket and trigger downstream processing.
Create an Eventarc trigger filtering for google.cloud.storage.object.v1.finalized events that routes directly to a Google Cloud Workflows definition, using built-in Google APIs connectors with try/except and switch blocks in YAML.
Deploy an Apache Airflow DAG in Cloud Composer that uses a GCSObjectsWithPrefixExistenceSensor to poll the bucket, triggering Cloud Run and Firestore using custom Python operators and Airflow retries.
Deploy a Cloud Run worker pool with minimum instances set to 0, running custom background bash scripts that periodically query the Cloud Storage API and trigger services.
Configure an Application Integration flow using a visual drag-and-drop mapper, setting a Cron schedule trigger to scan the storage bucket and trigger downstream processing.
Create an Eventarc trigger filtering for google.cloud.storage.object.v1.finalized events that routes directly to a Google Cloud Workflows definition, using built-in Google APIs connectors with try/except and switch blocks in YAML.
Google Cloud Workflows combined with Eventarc provides a fully managed, serverless orchestration framework specifically designed for low-latency, event-driven microservice and API coordination. The orchestration state machine is declared in YAML or JSON, allowing developers to sequence services, manage states, and implement custom error-handling logic.
google.cloud.storage.object.v1.finalized event directly from Cloud Storage and routes it to the target workflow within milliseconds, executing the pipeline on demand without polling delays.googleapis.firestore.v1 and googleapis.run.v1), which handle authentication, request formatting, and API interaction natively.try/except constructs, conditional switch branching, and automated retry policies with exponential backoff directly within the YAML specification.e.code == 404) and dynamically directs the execution path or halts with custom messages.Unlike container-based orchestrators that introduce node provisioning latency or batch-oriented workflow tools, Google Cloud Workflows starts immediately upon receiving the Eventarc trigger, providing sub-second invocation times and native connector support for Google Cloud APIs.
Deploy an Apache Airflow DAG in Cloud Composer that uses a GCSObjectsWithPrefixExistenceSensor to poll the bucket, triggering Cloud Run and Firestore using custom Python operators and Airflow retries.