Execute BigQuery Query as First Step in Kubeflow Pipeline

A.

When developing a Kubeflow pipeline on Google Kubernetes Engine, the easiest way to issue a query against BigQuery and use the results of that query as input to the next step in the pipeline is to use a custom component that uses the Python BigQuery client library to execute queries. Therefore, the correct answer is C: "Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries."

Here's why:

Option A suggests using the BigQuery console to execute the query and save the results into a new table. This may work for a one-time job, but it does not automate the process and would require manual intervention every time the pipeline is run. Additionally, it is not easily integrated with the Kubeflow pipeline.

Option B suggests writing a Python script that uses the BigQuery API to execute queries. While this is a valid approach, it would require additional work to integrate the script into the Kubeflow pipeline. The script would have to be containerized and then called as the first step in the pipeline.

Option D suggests using an existing BigQuery Query Component from the Kubeflow Pipelines repository. While this may be a valid approach if the component meets your specific requirements, it is not the easiest way to achieve the task at hand. Additionally, the repository may not have a component that exactly matches your needs.

Therefore, the best option is to use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries. This allows you to automate the process and easily integrate it into the pipeline.