1. Stream Analytics for AI with Pub/Sub and Dataflow Integration


    To enable stream analytics for AI with Pub/Sub and Dataflow integration on GCP using Pulumi, you need to configure and deploy several resources. These include a Pub/Sub topic to receive the streaming data, a Dataflow job to process the data, and often BigQuery or a similar data warehouse for analytics. The Pub/Sub system acts as the data ingestion layer, while Dataflow provides a managed service for executing a variety of data processing patterns. BigQuery (or an alternative) would be used to analyze and gain insights from the data.

    Below is a program in Python that sets up a streaming analytics pipeline using Pulumi with Google Cloud resources. The Dataflow job is created using the Dataflow template for Pub/Sub to BigQuery, which is a common use case for stream analytics. The template automatically sets up the necessary processing steps.

    import pulumi import pulumi_gcp as gcp # Create a Pub/Sub topic where data will be published. pubsub_topic = gcp.pubsub.Topic("analytics-topic") # Set up a BigQuery dataset and table for storing the processed information. bigquery_dataset = gcp.bigquery.Dataset("analytics_dataset", dataset_id="streaming_analytics_dataset", location="US") bigquery_table = gcp.bigquery.Table("analytics_table", dataset_id=bigquery_dataset.dataset_id, table_id="streaming_data", # Define the schema of the BigQuery table. schema="""[ { "name": "timestamp", "type": "TIMESTAMP", "mode": "NULLABLE" }, { "name": "device_id", "type": "STRING", "mode": "NULLABLE" }, { "name": "temperature", "type": "FLOAT", "mode": "NULLABLE" } ]""" ) # Configure the Dataflow job using the Pub/Sub Topic to BigQuery template. # Note: Adjust the template parameters according to your data. dataflow_job = gcp.dataflow.Job("analytics-dataflow-job", template_gcs_path="gs://dataflow-templates/latest/Stream_to_BigQuery", parameters={ "inputTopic": pubsub_topic.id.apply(lambda id: f"projects/{gcp.config.project}/topics/{id}"), "outputTableSpec": bigquery_table.id.apply(lambda id: f"{gcp.config.project}:{id}") }, temp_gcs_location="gs://<your-bucket>/temp", # Deprecated - use 'on_delete' with the 'drain' option instead. onDelete="DRAIN", region="us-central1" ) # Export the necessary resource names. pulumi.export("pubsub_topic_name", pubsub_topic.name) pulumi.export("bigquery_dataset_id", bigquery_dataset.dataset_id) pulumi.export("bigquery_table_id", bigquery_table.table_id) pulumi.export("dataflow_job_name", dataflow_job.name)

    This code snippet does the following:

    1. Defines a Pub/Sub topic that serves as the entry point for real-time event streams.
    2. Creates a BigQuery dataset and table to store processed data. The schema is defined inline.
    3. Specifies a Dataflow job using a pre-existing template that connects Pub/Sub to BigQuery.

    Please note the following:

    • In the Dataflow job's parameters, we use .apply to construct the Pub/Sub topic path and BigQuery table spec dynamically.

    • Replace <your-bucket> with your own Google Cloud Storage bucket name where Dataflow can store temporary files.

    • Ensure that the required APIs (Pub/Sub, Dataflow, and BigQuery) are enabled in your GCP project.

    • This code expects that Pulumi and GCP configurations are correctly set up, and you have appropriate permissions to create these resources.

    After deploying this Pulumi program, streaming data published to the Pub/Sub topic will be automatically processed by Dataflow and stored in the BigQuery table, ready for analytical queries and AI applications.