Your company is migrating its multi-cloud marketing analytics workflows to Google Cloud. At present, nightly ETL jobs are orchestrated by an on-premises Apache Airflow deployment that triggers Spark transformations on AWS EMR and stages the results in Amazon S3. Architects want to move only the orchestration layer to Google Cloud but keep using the same Airflow DAGs to avoid rewriting code and to prevent vendor lock-in. Built-in scheduling, retries, and Python operator support are mandatory. Which Google Cloud service best satisfies these requirements?
Cloud Composer is Google Cloud's fully managed service for Apache Airflow. Because it runs the open-source Airflow engine, existing DAG files can be migrated with minimal change, and the workflow definition remains portable to any other environment that supports Airflow, satisfying the requirement to avoid vendor lock-in. Composer also provides native scheduling, dependency management, and retry capabilities. Cloud Scheduler is limited to single-job cron scheduling. Workflows uses a proprietary YAML-based language and is not Airflow-compatible, requiring significant refactoring. Cloud Data Fusion focuses on GUI-driven ETL pipelines built on CDAP rather than executing arbitrary Airflow DAGs, so it would not meet the stated portability goal.
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What is Cloud Composer?
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Why is Apache Airflow DAG compatibility important in migration?
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How does Cloud Composer differ from Cloud Scheduler and Workflows?
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What is Apache Airflow, and why is it used in data workflows?
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How does Cloud Composer work with Apache Airflow DAGs?
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Why is Cloud Composer better suited for this scenario compared to Cloud Scheduler or Workflows?
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GCP Professional Data Engineer
Designing data processing systems
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