Staging Area for Data Ingestion into Azure Synapse Analytics

Question

Pulumi · Accepted Answer

To create a staging area for data ingestion into Azure Synapse Analytics, we will utilize several Pulumi resources to set up an Azure Synapse workspace, an Azure Data Lake Storage Gen2 account, an Azure Synapse SQL pool (formerly SQL DW), and necessary firewall and network configurations.

Here's how these components fit together:
- **Azure Synapse Analytics Workspace:** This is the dedicated SQL pool where you will be running your queries and managing your data. It represents a collection of analytic resources that are used together.
- **Azure Data Lake Storage Gen2:** Azure Synapse Analytics works in conjunction with Azure Data Lake Storage Gen2 to enable you to analyze a large amount of data.
- **Azure Synapse SQL pool (SQL DW):** This is the enterprise-level analysis service that you use to run big data SQL queries.
- **Firewall and Network Configurations:** For security, we need to establish rules about who can access our resources.

This setup is designed for you to start ingesting data into Azure Synapse Analytics for analytical purposes.

Now, let's go through the code to accomplish this. We will create resources using Pulumi and the `azure-native` provider.

```python
import pulumi
import pulumi_azure_native.synapse as synapse
import pulumi_azure_native.storage as storage
import pulumi_azure_native.resources as resources

# Define a resource group that will contain all our resources
resource_group = resources.ResourceGroup('rg')

# Create an Azure Data Lake Storage Gen2 account
data_lake_store = storage.StorageAccount('datalakestorage',
    resource_group_name=resource_group.name,
    kind='StorageV2',  # Required for Data Lake Storage Gen2
    sku=storage.SkuArgs(
        name='Standard_LRS'  # Locally redundant storage
    )
)

# Create a Synapse workspace
synapse_workspace = synapse.Workspace('synapseworkspace',
    resource_group_name=resource_group.name,
    storage_data_lake_gen2_filesystem_id=data_lake_store.id,
    sql_administrator_login='synapseadmin',
    sql_administrator_login_password='StrongPassword#1234',  # Replace with a secure password
    identity=synapse.ManagedIdentityArgs(
        type='SystemAssigned'
    )
)

# Create a Synapse SQL pool (formerly SQL DW)
sql_pool = synapse.SqlPool('sqlpool',
    resource_group_name=resource_group.name,
    workspace_name=synapse_workspace.name,
    sku=synapse.SkuArgs(
        name='DW100c'  # Choose an appropriate performance level
    ),
    collation='SQL_Latin1_General_CP1_CI_AS',
    create_mode='Default'
)

# Define Synapse workspace firewall rule to allow access from all IPs
# For production, restrict the IPs to only those necessary
all_ips_firewall_rule = synapse.IpFirewallRule('allowAll',
    resource_group_name=resource_group.name,
    workspace_name=synapse_workspace.name,
    start_ip_address='0.0.0.0',
    end_ip_address='255.255.255.255'
)

# Export the endpoints
pulumi.export('data_lake_store_endpoint', data_lake_store.primary_endpoints)
pulumi.export('synapse_workspace_endpoint', synapse_workspace.connectivity_endpoints.apply(lambda endpoints: endpoints['web']))
pulumi.export('sql_pool_id', sql_pool.id)
```

In the program above, we establish a new resource group and then create a storage account for our data lake, specifying `StorageV2` for Gen2 capabilities and using locally redundant storage for cost efficiency.

We then create the Synapse workspace and link the Data Lake. We also specify the SQL administrator login and password. Remember to replace the placeholder password with a strong, unique password in practice. The workspace is assigned a managed identity which means Azure will manage the credentials for us.

Next, we set up the Synapse SQL pool within the workspace. The performance level is specified (`DW100c` in this case, but you should choose based on your workload), and we set the collation for the SQL pool.

For our firewall rule, we allow access from all IP addresses for convenience during development. In a production scenario, it's crucial to restrict access to known IPs for security.

Finally, we export the important endpoints and resource IDs for easy access and reference.

Before you run this code, ensure that you have the Azure CLI installed and configured and that you've logged in and set your subscription. Also, install the Pulumi CLI and log in to the Pulumi service, this will manage the state of your resources. Then, run `pulumi up` to deploy the resources to Azure.

After you've run this program, you will have the necessary infrastructure in place to stage your data for ingestion into Azure Synapse Analytics. From there, you can start building pipelines to transform and analyze your data.