This generator reads the specified CDC table names, keys of the specified tables and merge table name and generates a merge query and a python script for each table for Cloud Composer or Apache Airflow.

The following steps must be completed before running this generator.

• An existing BigQuery Source Dataset that holds all source tables, each of which with recordtimestamp and operation columns
• A running Cloud Composer Instance in the target Google Cloud project
• A tested Airflow BQ Connection to the source Google Cloud Project
• A GCS bucket created for holding the DAG python scripts and SQL scripts
• A GCS bucket created for logs that this generator writes to

The cloudbuild.yaml for this generator requires the following parameters

• _DS_SRC: Source BigQuery Dataset
• _DS_TGT: Target BigQuery Dataset
• _PJID_SRC: Source Google Cloud Project ID
• _PJID_TGT: Target Google Cloud Project ID
• _GCS_BUCKET: Name of the bucket created for transient holding the DAG scripts and SQL scripts
• _GCS_LOG_BUCKET: GCS bucket created for logs that this generator writes to

• Clone the repository into your Cloud Shell Editor or an IDE of your choice [Ensure gcloud SDK isinstalled, if you choose your own IDE]
• Make required changes in the settings.yaml to add / delete the required tables and run frequencies. Save the file.

The generator can be run from the Cloud Console using the gcloud builds submit ... command or by configuring a Cloud Builds trigger that runs automatically upon push to a Cloud Source Repository branch

• The generated python scripts will be copied to gs://${_GCS_BUCKET}/dags
• The generated SQL scripts will be copied to gs://${_GCS_BUCKET}/data/bq_data_replication