Automated Data Lineage for AI Pipelines with Azure Purview

Question

Pulumi · Accepted Answer

To set up automated data lineage for AI pipelines with Azure Purview, you will need to create an instance of Azure Purview, which is a unified data governance service that helps you manage and govern your on-premises, multitier, and software as a service (SaaS) data. Azure Purview offers automated data lineage capabilities that help you understand the source, movement, and transformation of your data across hybrid landscapes.

In the following Pulumi program, we will create a Purview Account which is the central resource in Azure Purview and serves as a container for your data governance service. This is the first step required to establish automated data lineage. After setting this up, you can use Purview's features to automatically capture data lineage information as data flows through various processes and transformations within your AI pipelines.

Here's a step-by-step guide to creating a Purview Account using Pulumi with Python:

```python
import pulumi
import pulumi_azure_native as azure_native

# First, create a resource group to contain the Purview Account.
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Now, create the Purview Account within the resource group.
purview_account = azure_native.purview.Account("purviewAccount",
    resource_group_name=resource_group.name,
    location="eastus",  # Azure region where you want to deploy Purview.
    identity=azure_native.purview.IdentityArgs(
        type="SystemAssigned",  # Specifies an identity type - "SystemAssigned" means Azure will create and manage the identity.
    ),
    sku=azure_native.purview.SkuArgs(
        name="Standard",  # Sku tier - "Standard" should be sufficient for most needs.
        capacity=4  # Units of capacity, adjust based on the needed scale.
    ),
    public_network_access="Enabled",  # Indicates if the public network access is allowed.
)

# Export the Purview Account name and the Principal ID of the account's identity; this will be useful for granting permissions.
pulumi.export("purview_account_name", purview_account.name)
pulumi.export("purview_principal_id", purview_account.identity.apply(lambda identity: identity.principal_id))
```

In the above program, we start by importing Pulumi's Azure Native module, which allows us to work with Azure resources. We then create a new resource group to hold our Azure Purview Account. After that, we create the Purview Account itself. We specify a `SystemAssigned` identity, which means Azure will create an identity for this account and manage it.

Additionally, we allocated a Sku capacity based on how much we think we’ll need. You should adjust this based on the expected workload and the scale required for your specific AI pipelines. The `public_network_access` parameter is set to `Enabled` to allow access over the public network. This is typical but should be carefully considered with regard to your organization's network security policies.

Finally, we export the name of the created Purview Account and the principal ID of the account's identity. These outputs can be used to set up permissions or to reference the Purview Account in subsequent resource deployments that might be part of your data governance pipeline.

The `.apply` method is utilized here to extract the `principal_id` from the identity of the Purview Account once it's available after the account is created. This is a common Pulumi pattern for handling outputs that depend on the creation of other resources.

To manage the data lineage with Azure Purview, you would typically integrate it with your AI and data processing services, like Azure Machine Learning or Azure Data Factory pipelines. Purview will then automatically track how data is transformed and moved across these services, helping maintain clear and compliant data governance.