1. Scheduled Transfer from SaaS Apps to BigQuery for Analysis


    To set up a scheduled transfer from SaaS applications to Google BigQuery for analysis using Pulumi, you'll typically need to follow these steps:

    1. Create a BigQuery Dataset: A dataset is a top-level container used to organize and control access to your tables and views in BigQuery. It's the place where you'll store the data transferred from your SaaS application.
    2. Set up a Data Transfer Service: Google's Data Transfer Service allows you to automate data movement into BigQuery on a scheduled, managed basis. You'll need to configure a transfer service that understands how to connect to your specific SaaS application and pull data from there.
    3. Configure Access and Permissions: For the transfer service to write to your BigQuery dataset, you must ensure it has the necessary permissions to perform the actions.

    Below you'll find a Pulumi Python program that provisions a BigQuery dataset and sets up a Data Transfer Service job for this purpose.

    Before you begin, ensure you have the following prerequisites met:

    • A Google Cloud Platform project
    • Pulumi CLI installed and configured with access to your GCP project
    • Necessary APIs enabled in your GCP project (BigQuery and Data Transfer API)

    The following program will create a dataset inside your BigQuery and outline how to initialize the setup for a Data Transfer Service job. Actual connections to SaaS providers vary per service, so you'll need to refer to Google's documentation for specific parameters required for your SaaS app.

    import pulumi import pulumi_gcp as gcp # Create a BigQuery dataset to hold the transferred data bigquery_dataset = gcp.bigquery.Dataset("my_dataset", dataset_id="my_dataset", location="US", # Choose your dataset's location. List of locations: https://cloud.google.com/bigquery/docs/locations ) # Configuration for the Data Transfer Service job # This is where you'd include specific parameters for your SaaS application transfer # It is included here as a placeholder and needs to be adjusted for your specific use case data_transfer_config = { # 'dataSourceId': 'your_saaS_application_id provided by Google', # E.g., 'google_adwords' for Google Ads # 'params': { # 'param_key': 'param_value', # Parameters such as credentials, paths etc., specific to your SaaS app # }, # 'schedule': 'every 24 hours', # Determine your transfer schedule } # Provision the Data Transfer Service job # Uncomment and populate with your actual data source and parameters # data_transfer_job = gcp.bigquery.DataTransferConfig("my_data_transfer_job", # destination_dataset_id=bigquery_dataset.dataset_id, # display_name="My Data Transfer Job", # data_source_id=data_transfer_config['dataSourceId'], # params=data_transfer_config['params'], # schedule=data_transfer_config['schedule'], # project=bigquery_dataset.project, # Uses the project from the dataset resource # ) # Export the dataset ID and Data Transfer job name (if created) pulumi.export("dataset_id", bigquery_dataset.dataset_id) # pulumi.export("data_transfer_job_name", data_transfer_job.name) # Uncomment when the data transfer job is created


    • BigQuery Dataset: gcp.bigquery.Dataset is used to create a new dataset within BigQuery, which is where the imported data will be stored.
    • Data Transfer Service: gcp.bigquery.DataTransferConfig would be used to create a new data transfer job configuration. It's currently commented out because the actual service (dataSourceId) and parameters (params) are highly dependent on the specific SaaS app you're transferring from. Be sure to replace these placeholders with actual values for your use case.
    • Scheduling: The schedule specifies how often the data transfer job should run. The schedule syntax is in a form similar to the Unix cron format.

    Remember, the above code is a framework to guide the process, but you will need to replace placeholders with actual values based on the SaaS provider's requirements. Please refer to the Google Cloud documentation for scheduled transfers for further details on configuring the transfer job for your specific SaaS application.

    Also, ensure that the service account associated with Pulumi has the appropriate permissions to create resources within your GCP project and BigQuery dataset.