1. Real-time Predictive Analytics using Databricks Jobs


    To implement real-time predictive analytics using Databricks Jobs, you would need to create a Databricks workspace, cluster, and job that runs your analytics code. In this Pulumi program, I will guide you through setting up the necessary resources for real-time predictive analytics using the Databricks provider in Pulumi.

    First, you would need to create a Databricks workspace which is a fundamental resource where all your Databricks assets like clusters, notebooks, and jobs reside. For this example, I am skipping the workspace setup as it is typically done at the organization level and may already be available for you to use.

    Next, you would define a cluster. A Databricks cluster is a set of computation resources and configurations on which you run your data analytics workloads, such as Spark jobs, SQL queries, and more.

    Finally, you would create a job. Databricks jobs are used to schedule and run computational workloads, like Spark jobs or notebooks. For real-time predictive analytics, the job might include tasks to process data streams, apply machine learning models to this data, and output predictions.

    Below is a simplified Pulumi program written in Python that demonstrates how you might declare a Databricks job that could be used for real-time predictive analytics.

    import pulumi import pulumi_databricks as databricks # Configure the Databricks provider using the workspace URL and personal access token. pulumi.Config.require("databricks_workspace_url") pulumi.Config.require_secret("databricks_personal_access_token") # Set the Databricks workspace URL and access token from configuration (or environment variables). # Note: Workspace URL and Personal Access Token are sensitive information and should be managed securely. databricks_provider = databricks.Provider( "databricks_provider", host=pulumi.Config().require("databricks_workspace_url"), token=pulumi.Config().require_secret("databricks_personal_access_token"), ) # Define a new cluster that the job will use. cluster = databricks.Cluster( "analytics-cluster", cluster_name="real-time-analytics-cluster", spark_version="latest", # Choose the latest Spark version suitable for real-time analytics. node_type_id="Standard_D3_v2", # Choose a node type appropriate for your workload. num_workers=2, # Define the number of worker nodes in the cluster. autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=10, ), opts=pulumi.ResourceOptions(provider=databricks_provider), ) # Define a new job to run predictive analytics tasks. job = databricks.Job( "predictive-analytics-job", name="predictive-analytics", # Here you can define your tasks, which could be a series of notebooks, JARs, or Python scripts that analyze the data. tasks=[ databricks.JobTaskArgs( task_key="main_task", new_cluster=databricks.JobTaskNewClusterArgs( spark_version="latest", node_type_id="Standard_D3_v2", num_workers=2, ), spark_python_task=databricks.JobTaskSparkPythonTaskArgs( python_file="dbfs:/FileStore/path/to/your/python/script.py", parameters=["--param", "value"], # Add necessary parameters for your analytics script. ), ), ], # Optional email notification settings. email_notifications=databricks.JobEmailNotificationsArgs( on_failure=["your-email@example.com"], no_alert_for_skipped_runs=True, ), max_concurrent_runs=1, # Allows only one run of the job at a time. opts=pulumi.ResourceOptions(provider=databricks_provider), ) # Export the job ID as an output. pulumi.export("job_id", job.id)

    In this program, a new Databricks cluster and job are created using the pulumi_databricks Python module. The job is defined to run a Python script located in your Databricks FileStore, and the cluster is set to auto-scale from 2 to 10 worker nodes. For real predictive analytics work, you would replace the script with your machine learning model's inference script that processes your real-time data.

    Remember to set up your Pulumi configuration or use environment variables for your Databricks workspace URL and a personal access token. These details are necessary for Pulumi to communicate with your Databricks workspace and manage resources there.

    If you have any questions about specific configurations or how to adapt this for your particular use case, feel free to ask!