You have three jobs (Dataflow transform->BigQuery, on-prem ingest->Cloud Storage, Dataflow third-party->Cloud Storage) that require scheduling, monitoring, and manual runs. Which orchestration approach do you use?

Cloud Composer, based on Apache Airflow, is designed for orchestrating complex workflows, including scheduling, monitoring, and managing dependencies between jobs. It uses Directed Acyclic Graphs (DAGs) to define these workflows, making it ideal for the described scenario involving Dataflow and Cloud Storage operations. Stackdriver Monitoring is primarily for observability and alerting, not for job orchestration or scheduling. While it can trigger actions via webhooks, it lacks the robust scheduling and dependency management capabilities of Cloud Composer. App Engine can host applications that call GCP APIs, but developing a custom orchestration solution would be more complex and less feature-rich than using a managed service like Cloud Composer. Similarly, using cron jobs on a Compute Engine instance provides basic scheduling but lacks integrated monitoring, dependency management, and the scalability of a dedicated orchestration platform.