Data Movement for AI Pipelines Using Azure Data Factory

Question

Pulumi · Accepted Answer

Data movement in an AI pipeline is crucial for transporting processed and raw data between different storage services and computational environments. Azure Data Factory (ADF) serves as the orchestrator for such data movements, enabling the creation, scheduling, and management of data flow processes.

The following Pulumi program demonstrates how to set up data movement components within Azure Data Factory, using Pulumi's Python SDK. To move the data, we will define several key components:

Factory: Represents the Data Factory itself, the central resource where data pipelines are executed.
LinkedServiceDataLakeStorageGen2: Defines a linked service pointing to an Azure Data Lake Storage Gen2 account, which acts as a source or sink for data.
Pipeline: Orchestrates data movement using activities that define actions to perform on the data, like copying from the source to the destination.

Here is a simple program that sets up a pipeline to move data from a Data Lake Storage Gen2 to another linked service destination in Azure Data Factory:

import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group, if not already existing
resource_group = azure_native.resources.ResourceGroup('my-resource-group')

# Create an Azure Data Factory
data_factory = azure_native.datafactory.Factory("myDataFactory",
    resource_group_name=resource_group.name,
    location=resource_group.location)

# Define a Data Lake Storage Gen2 linked service in the Data Factory
data_lake_storage_linked_service = azure_native.datafactory.LinkedServiceDataLakeStorageGen2("myDataLakeStorageLinkedService",
    resource_group_name=resource_group.name,
    factory_name=data_factory.name,
    properties=azure_native.datafactory.LinkedServiceDataLakeStorageGen2TypeProperties(
        url="https://mydatalakestorage.blob.core.windows.net",
        service_principal_id="<service-principal-id>",
        service_principal_key="<service-principal-key>",
        tenant="<tenant-id>",
    ))

# Define a Data Factory Pipeline
pipeline = azure_native.datafactory.Pipeline("myDataPipeline",
    resource_group_name=resource_group.name,
    factory_name=data_factory.name,
    properties=azure_native.datafactory.PipelineProperties(
        activities=[
            # Add activities here such as copying data from one linked service to another
        ]
    ))

In the example above, we start with the creation of an Azure Resource Group to house our resources. Following this, we instantiate Factory to create a Data Factory instance. We then define LinkedServiceDataLakeStorageGen2, which represents a linked service to an Azure Data Lake Storage Gen2. It's essential to replace <service-principal-id>, <service-principal-key>, and <tenant-id> with your actual service principal credentials that have access to the data lake.

The Pipeline resource leverages the activities list to define what operations to perform with the data. In a real-world scenario, this part will contain activities such as CopyActivity which define the source, transformations, and destination of data.

Remember to export the URLs or any other identifiers you might need to access or interact with your Data Factory:

# Exporting the Data Factory URL
pulumi.export('data_factory_url', data_factory.ui_url)

In this setup, we have not included any specific data movement activities, as they would need to be tailored to your exact use case, including the desired transformations, and source and target datasets.

Given that you're a novice, you may wish to explore the Pulumi Azure Native documentation for more detailed examples of how to configure the various activities within an Azure Data Factory pipeline, according to the specifics of your AI data pipeline.