1. Dataflow for Stream and Batch Data Processing

    Python

    Dataflow is a managed service offered by Google Cloud Platform that enables developers to build, deploy, and run data-processing workflows. It facilitates both batch and stream (real-time) processing, with the ability to automatically scale resources to match the workloads' demands.

    Pulumi enables you to define, deploy, and manage Google Cloud Platform resources using real programming languages, including Python. When you want to create a Dataflow job using Pulumi, you can use the gcp.dataflow.Job resource from the pulumi_gcp package.

    Here's an outline of steps we'll follow to create a Dataflow job for stream and batch data processing using Pulumi with Python:

    1. Import the required Pulumi and GCP packages.
    2. Define a Dataflow job using the gcp.dataflow.Job resource.
      • We will specify typical properties such as the job name, region, zone, network, and more importantly, the templateGcsPath - which is the path to the Dataflow template on Google Cloud Storage. This template defines the actual data processing job to be executed.
    3. Export any necessary outputs, such as the Dataflow job's ID or state.

    Below is the Pulumi program written in Python that achieves this:

    import pulumi import pulumi_gcp as gcp # Replace the following with the appropriate values for your use case. project = 'my-gcp-project' # Google Cloud Project ID region = 'us-central1' # Google Cloud region where the job should run gcs_temp_location = 'gs://my-bucket/temp' # Google Cloud Storage path for temp files generated by Dataflow dataflow_template_path = 'gs://dataflow-templates/latest/Word_Count' # Replace with the path to your Dataflow template # Create a Dataflow job for batch or stream processing dataflow_job = gcp.dataflow.Job("dataflow-job", project=project, region=region, temp_gcs_location=gcs_temp_location, template_gcs_path=dataflow_template_path, # Additional parameters can be provided depending on the template and use case. parameters={ 'inputFile': 'gs://my-bucket/input-data.txt', # Input file (change as needed) 'outputFile': 'gs://my-bucket/output-results/' # Output location (change as needed) }, ) # Export the Dataflow job's ID, which can be useful for monitoring or management purposes. pulumi.export('job_id', dataflow_job.id)

    In this program:

    • We define a dataflow_job as a gcp.dataflow.Job. The properties we assign correspond to the required parameters and settings for running a Dataflow job.
    • The parameters dictionary includes job-specific parameters required by the Dataflow template, such as input and output locations.
    • We then export the job's ID for future reference.

    Remember that this program assumes that you have already set up GCP credentials and configured the Pulumi GCP plugin. The Dataflow job defined here uses a publicly available word count template, but in a real-world scenario, you would replace the template_gcs_path and parameters with those specific to your data processing pipeline.

    For more information on Pulumi and the resources available in the pulumi_gcp package, you can refer to the Pulumi documentation for the GCP provider.


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