1. Real-time AI Data Analytics with GCP Dataproc Streaming


    In order to set up real-time AI data analytics with Google Cloud Platform (GCP) Dataproc Streaming, you would typically use Google Dataproc to run data processing workloads, such as Apache Spark and Apache Hadoop jobs. Real-time analytics can be achieved by utilizing streaming capabilities with Spark Streaming or similar technologies available in Dataproc.

    Below is a Pulumi Python program that demonstrates how to create a Dataproc cluster which can be used for running streaming jobs for real-time analytics. This program also shows how to submit a sample Spark Streaming job as an example of real-time data processing.

    The resources used in this program are:

    • Cluster: This resource represents a Dataproc cluster, which is a managed Hadoop and Spark service that allows you to run big data workloads. In real-time data analytics scenarios, this cluster would process data streams.
    • Job: This resource represents a job submission to the Dataproc cluster. In this case, the job will be a Spark Streaming job.

    The program assumes that you have already set up GCP credentials and configured the Pulumi GCP plugin with the necessary project and region settings.

    import pulumi import pulumi_gcp as gcp # Create a Dataproc cluster optimized for running data analytics workloads dataproc_cluster = gcp.dataproc.Cluster("analytics-cluster", region="us-central1", # Example region - choose the one that suits your needs cluster_config={ "master_config": { "num_instances": 1, "machine_type": "n1-standard-1", # Example machine type - choose based on your workload }, "worker_config": { "num_instances": 2, "machine_type": "n1-standard-1", # Same as master - keep consistent or modify as needed }, # Additional configurations can be set here depending on the needs }) # Submit a Spark Streaming job to the cluster # You would replace "file:///path/to/your/job.py" with the location of your streaming job script spark_streaming_job = gcp.dataproc.Job("spark-streaming-job", region="us-central1", # Must match the cluster's region placement={"cluster_name": dataproc_cluster.name}, spark_job={ "main_class": "org.apache.spark.examples.streaming.NetworkWordCount", "args": ["localhost", "9999"], # Example args - replace them with your job's requirements # You would provide the jar file URIs required for your job: "jar_file_uris": ["file:///path/to/your/job.jar"], # `file_uris` may include your job script, dependencies, or data files "file_uris": ["file:///path/to/your/job.py"], }) # Export the Dataproc cluster's id pulumi.export("dataproc_cluster_id", dataproc_cluster.id) # Export the Spark Streaming job's id pulumi.export("spark_streaming_job_id", spark_streaming_job.id)

    This program first creates a Dataproc cluster with one master and two worker nodes, which is a typical small configuration for real-time analytics, though for production you would likely increase both the number and power of the nodes. Then it submits a Spark Streaming job to this cluster. The example uses a built-in Spark example class NetworkWordCount, but in a real-world scenario, you would replace the main_class, args, and jar_file_uris with the ones relevant to your workload.

    You need to create a streaming job .py or .jar file that implements your real-time analytics logic and specify the correct path to that file in the spark_streaming_job job resource configuration.

    To implement AI data analytics with Dataproc Streaming, you would integrate Spark with a machine learning library, such as MLlib for Spark, or use external AI services provided by GCP like AI Platform for training and inferencing. The data input and output would need to be configured to stream the desired data sources into the job for processing and then out to whatever services or storage solutions you require for your analytics results.

    Please replace placeholder values like "us-central1" and file paths with your actual cluster parameters, job locations, and GCP project information before running the program.