Managed Relational Database for AI Workload Reporting on Azure SQL

Question

Pulumi · Accepted Answer

Creating a managed relational database on Azure SQL suitable for an AI workload reporting scenario involves a couple of steps. Azure SQL offers managed database capabilities, allowing you to focus on the data and application side of things while Azure takes care of the maintenance, backups, and scaling. For AI workloads specifically, it's important to have a database that can handle large queries with fast response times and robust security.

In our Pulumi program, we're going to create a managed instance of Azure SQL Database, a secure and scalable relational database service. Managed instances provide compatibility with SQL Server, which is ideal for migrating existing SQL Server databases to the cloud with minimal changes. They are also a good choice for new applications that require rich SQL Server surface area.

Here's how you create a managed instance and a corresponding database in Azure SQL with Pulumi:

1. Import the required modules.
2. Create an instance of `ManagedInstance`, which represents the managed SQL Server.
3. Create a `ManagedDatabase` within the managed instance we've just declared.

In this Python program, we are specifically using the `azure-native` package to leverage Azure's native functionalities. We create an SQL Managed Instance and a Managed Database within this instance. Notice that I assign some placeholder values in the arguments. You will need to replace 'your_location', 'your_resource_group', 'unique_managed_instance_name', and 'unique_database_name' with the actual values for your project before running this program.

Below is the program which sets up the managed database:

```python
import pulumi
from pulumi_azure_native import resources
from pulumi_azure_native import sql as azure_sql

# Create an Azure Resource Group
resource_group = resources.ResourceGroup('resource_group', 
    resource_group_name='your_resource_group')

# Create an Azure SQL Managed Instance
managed_instance = azure_sql.ManagedInstance('managed_instance',
    resource_group_name=resource_group.name,
    location='your_location',  # Choose the appropriate Azure location here.
    managed_instance_name='unique_managed_instance_name',
    # The below arguments define the characteristics of the managed instance,
    # such as hardware, compute size, and instance settings. Adjust them per your use case.
    sku=azure_sql.SkuArgs(
        name="GP_Gen5",
        tier="GeneralPurpose",
    ),
    administrators=azure_sql.ManagedInstanceAdministratorsArgs(
        administrator_type="ActiveDirectory",
        login="AdminUser",
        principal_id="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx", # Your principal ID goes here
        sid="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx", # Your SID goes here
        tenant_id="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" # Your tenant ID goes here
    ),
    storage_size_in_gb=512  # Defines the size of the storage attached to the instance.
)

# Create an Azure SQL Database within the Managed Instance
managed_database = azure_sql.ManagedDatabase('managed_database',
    resource_group_name=resource_group.name,
    managed_instance_name=managed_instance.name,
    database_name='unique_database_name', # Give your database a unique name.
)

# Output the managed SQL database ID
pulumi.export('managed_database_id', managed_database.id)

# Output the managed SQL instance ID
pulumi.export('managed_instance_id', managed_instance.id)
```

In this program:

- We declare an Azure Resource Group to organize related resources. This can be your existing resource group where you organize your Azure resources.
- The Managed Instance, `managed_instance`, is created with specified `sku` and `administrators`, where `sku` defines the performance characteristics, and `administrators` are the assigned admins for this instance.
- Within the Managed Instance, we create the actual database, `managed_database`.

The `pulumi.export` statements at the end will output the IDs of the managed database and managed instance as stack outputs once the program is deployed. These can be useful to reference these resources in subsequent commands or other Pulumi stacks.

Remember to configure your Pulumi environment and Azure CLI with the necessary credentials to deploy this infrastructure. Also, please make sure to fill in the real values instead of placeholders for the actual deployment.