Recommended Approach to schedule Dataflow Pipeline in python

[dvdepassessment]

Hi All,

I wrote a Python script that loads data from an HTTP API into BigQuery using a Dataflow pipeline (Apache Beam). Currently, I am running the script manually in Cloud Shell.

I would like to know the recommended approach for scheduling this pipeline to run on a regular basis.

Thanks,

[nikacalupas]

Hi dvdepassessment,

Welcome to the Google Cloud Community!

Here are some recommended approaches that might help you schedule your Dataflow pipeline to run on a regular basis:

• Cloud Scheduler + Cloud Functions: Cloud Scheduler triggers a Cloud Function to start your Dataflow pipeline using the Dataflow API. It's best for simple, standalone jobs with basic scheduling needs, like running daily. This approach is easy to set up and cost-effective for infrequent runs. 
• Cloud Scheduler + Cloud Pub/Sub + Cloud Function/Dataflow: Works by having Cloud Scheduler publish a message to a Pub/Sub topic. A Cloud Function or another Dataflow job then subscribes to that topic and starts the pipeline. This approach is ideal when you need to decouple the scheduler from the pipeline execution, such as when triggering the pipeline based on an event or using multiple triggers.
• Apache Airflow (Cloud Composer): Uses Directed Acyclic Graphs (DAGs) to define and schedule complex workflows, where tasks within the DAG can trigger your Dataflow pipeline. This approach is best suited for complex scheduling needs, such as handling task dependencies, retries, error handling, and orchestrating multiple tasks in a larger data processing workflow. Airflow provides powerful orchestration, scheduling, and monitoring capabilities, and integrates well with other Google Cloud services.

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