1. Real-time Analytics with Azure Synapse Apache Spark Pools


    Azure Synapse is an analytics service that brings together big data and data warehousing. It offers the ability to query data using on-demand or provisioned resources, at scale. Azure Synapse also integrates Apache Spark, which is an open-source, distributed processing system used for big data workloads. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

    In this Pulumi program, we'll define an Azure Synapse Workspace along with an Apache Spark pool within it. This will provide the groundwork for setting up real-time analytics.

    Here are the steps we'll follow in the program:

    1. Set up a new Azure Synapse Workspace.
    2. Configure an Apache Spark Pool within the Workspace.
    3. Export relevant outputs that can be used to access the Synapse Workspace and Spark Pool.

    The Pulumi resource we use to create a Synapse Workspace is azure-native.synapse.Workspace. For the Apache Spark Pool, we use azure-native.synapse.BigDataPool.

    Let me guide you through the creation of these resources using Pulumi in Python:

    import pulumi from pulumi_azure_native import resources, synapse # Create an Azure Resource Group to contain our Synapse Workspace resource_group = resources.ResourceGroup("synapse-resource-group") # Create an Azure Synapse Workspace synapse_workspace = synapse.Workspace("synapseWorkspace", resource_group_name=resource_group.name, location=resource_group.location, identity=synapse.ManagedIdentityArgs( type="SystemAssigned", ), sql_administrator_login="sqladminuser", sql_administrator_login_password="MyReallyStrongPassword#2024" # Additional properties can be set as needed. ) # Create an Apache Spark Pool within the Synapse Workspace spark_pool = synapse.BigDataPool("sparkPool", resource_group_name=resource_group.name, workspace_name=synapse_workspace.name, spark_version="2.4", node_size_family="MemoryOptimized", node_size="Large", node_count=4, # The number of nodes in the Spark pool. # Additional properties can be set as needed. ) # Export the outputs for the Synapse Workspace and Spark Pool pulumi.export("synapse_workspace_name", synapse_workspace.name) pulumi.export("spark_pool_name", spark_pool.name)

    In this program:

    • We start by instantiating an Azure Resource Group which acts as a container for our Synapse Workspace.
    • The Synapse Workspace is created using synapse.Workspace and is placed within the Resource Group we defined earlier. We assign it a Managed Identity type of "SystemAssigned" for Azure services to access other Azure resources.
    • Within the Workspace, we create an Apache Spark pool using synapse.BigDataPool, which provisions the computational resources needed to process big data tasks.
    • Lastly, we use pulumi.export to make the names of both the Synapse Workspace and Spark Pool available outside of Pulumi after the deployment.

    You'll notice that we've used a placeholder for the sql_administrator_login_password. In a production scenario, you should never hard-code passwords. Instead, use Pulumi's configuration system or a secret manager to inject secrets at deployment time securely.

    Before you can run this program, you must have the Pulumi CLI installed and configured for Azure. Then, within the directory of your Pulumi project, run pulumi up. Pulumi will perform a preview run and then prompt you to confirm the deployment, which will create the resources on Azure.