Real-time Analytics with Azure Synapse Apache Spark Pools

Question

Pulumi · Accepted Answer

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:

```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.