1. Real-time Analytics with Databricks Delta Live Tables


    To set up a real-time analytics system with Databricks Delta Live Tables, you would typically need a Databricks workspace running on a cloud provider like Azure or AWS. Since Databricks itself is not directly represented in the Pulumi resources, this example will focus on provisioning the necessary cloud infrastructure using Azure as the cloud provider.

    First, we would create an Azure Synapse workspace, which allows you to prepare data for analytics. Although it's not Delta Live Tables, Synapse includes Apache Spark pools which can be used for big data processing and analytics in a way that is similar to Databricks. Next, we'd set up an HDInsight cluster, which lets you use popular open-source frameworks including Apache Hadoop, Spark, Kafka, and more. Finally, you could use these components to ingest data, process it in real-time, and make it available for analytics and machine learning.

    Let's start by setting up an Azure Synapse workspace and an HDInsight Spark cluster. Here's a program that does that using Pulumi with Python:

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import hdinsight # Create an Azure resource group resource_group = azure_native.resources.ResourceGroup('rg') # Create an Azure Synapse Workspace synapse_workspace = azure_native.synapse.Workspace( "synapseWorkspace", resource_group_name=resource_group.name, location=resource_group.location, identity=azure_native.synapse.WorkspaceIdentityArgs( type="SystemAssigned" ), sql_administrator_login="sqladminuser", sql_administrator_login_password="MyP@ssw0rd", ) # Create an HDInsight Spark cluster spark_cluster = hdinsight.Cluster( "sparkCluster", resource_group_name=resource_group.name, properties=hdinsight.ClusterCreatePropertiesArgs( cluster_version="3.6", os_type="Linux", tier="Standard", cluster_definition=hdinsight.ClusterDefinitionArgs( kind="spark", configurations={ "gateway": { "restAuthCredential.isEnabled": "true", "restAuthCredential.username": "admin", "restAuthCredential.password": "AdminPassword1!" } } ), compute_profile=hdinsight.ComputeProfileArgs( roles=[ hdinsight.RoleArgs( name="headnode", target_instance_count=2, hardware_profile=hdinsight.HardwareProfileArgs( vm_size="Standard_D3_v2" ), os_profile=hdinsight.OsProfileArgs( linux_operating_system_profile=hdinsight.LinuxOperatingSystemProfileArgs( username="clusteruser", password="ClusterPassword1!" ), ), ), hdinsight.RoleArgs( name="workernode", target_instance_count=4, hardware_profile=hdinsight.HardwareProfileArgs( vm_size="Standard_D3_v2" ), os_profile=hdinsight.OsProfileArgs( linux_operating_system_profile=hdinsight.LinuxOperatingSystemProfileArgs( username="clusteruser", password="ClusterPassword1!" ), ), ) ] ), storage_profile=hdinsight.StorageProfileArgs( storageaccounts=[ hdinsight.StorageAccountArgs( name="mydata.blob.core.windows.net", is_default=True, container="my-container", key="<storage-account-key>" ) ] ), ), location=resource_group.location, ) # Output the Azure Synapse Workspace URL and the HDInsight Spark cluster endpoint pulumi.export("synapseWorkspaceUrl", pulumi.Output.concat("https://web.azuresynapse.net/?workspace=", synapse_workspace.name)) pulumi.export("sparkClusterEndpoint", spark_cluster.properties.apply( lambda props: props.connectivity_endpoints[0].location if props and props.connectivity_endpoints else None))

    In this program:

    1. Resource Group: We start by creating an Azure resource group, which will contain all our resources.
    2. Synapse Workspace: We then create an Azure Synapse workspace, which includes Spark and other services for processing data.
    3. HDInsight Spark Cluster: Next, we create an HDInsight cluster configured for Spark, which we will use to process data in real-time.
    4. Outputs: We provide outputs so you can easily find the URLs to access the Synapse workspace and the Spark cluster.

    For real-time analytics and stream processing, you would use Spark's streaming capabilities within the HDInsight cluster to process incoming data in real-time.

    Please ensure that you replace the placeholders like <storage-account-key> with your actual storage account key. You might also want to change username/password fields as per your organizational security policies.

    Remember, while the above program sets up the necessary infrastructure for data processing, you would need additional steps to set up Delta Live Tables and configure it for your use case. Unfortunately, Pulumi currently does not have direct support for setting up Databricks Delta Live Tables, but you can use Pulumi to create the initial environment and use Databricks' own tooling to configure Delta Live Tables.