ETL Workflows for AI Model Training with Azure Data Factory Dataflows

Question

Pulumi · Accepted Answer

To create ETL (Extract, Transform, Load) workflows for AI model training with Azure Data Factory, you can use Pulumi to define, deploy, and manage the necessary Azure resources in a declarative manner using Python. In this context, "ETL" refers to the process of extracting data from various sources, transforming this data into a usable format, and then loading it into a destination for analysis and AI model training.

Azure Data Factory is a cloud service provided by Microsoft Azure that enables you to create data-driven workflows for orchestrating and automating data movement and data transformation. Specifically, Data Factory Dataflows allow you to develop complex ETL processes that are visually designed and can be used to manipulate and transform data.

Before we dive into the code, make sure you have the following prerequisites in place:

1. Pulumi CLI installed: You need to have the Pulumi CLI installed on your machine and have access to an Azure subscription.
2. Python: The code provided will be in Python, so you should have Python installed as well as an environment where you can run Python code.
3. Pulumi Azure Native provider configured: You will need to log in to your Azure account via the Pulumi CLI using `pulumi login`.

Let's start by understanding the main resources we're going to create:

- **Azure Data Factory**: The primary resource that acts as the ETL orchestrator. You define a data-driven workflow that moves and transforms data between supported data stores.
- **Linked Services**: These are connection strings that link your data factory to your data sources and sinks (destinations).
- **Datasets**: Definitions of your data sources and destinations that are referenced in Dataflows and pipelines.
- **Pipelines**: Define the workflow of activities. In this case, the main activity is a Dataflow that performs the actual data transformation.
- **Dataflow**: The transformation logic or the 'T' in ETL.

Now let's get into creating these resources with Pulumi. Here's a high-level Python program that stitches together an ETL workflow for AI model training using Azure Data Factory Dataflows:

```python
import pulumi
import pulumi_azure_native as azure_native

# Creating an instance of Azure Data Factory
data_factory = azure_native.datafactory.Factory(
    # Define the factory name and the resource group it belongs to
    factory_name="myadf",
    resource_group_name="myResourceGroup",
    # Location must be global or the region of the resource group.
    location="East US",
)

# Creating a Linked Service to Azure Storage where raw data may reside.
# Please adjust the connection string and any other parameters accordingly.
# You might prefer to fetch connection string from KeyVault in a secure deployment.
linked_service = azure_native.datafactory.LinkedService(
    name="StorageLinkedService",
    linked_service_name="mystorageaccount",
    resource_group_name="myResourceGroup",
    properties=azure_native.datafactory.LinkedServiceTypeProperties(
        type="AzureStorage",
        type_properties=azure_native.datafactory.AzureStorageLinkedServiceTypeProperties(
            connection_string="DefaultEndpointsProtocol=https;AccountName=mystorageaccount;AccountKey=myAccountKey",
        )
    )
)

# Creation of input dataset pointing to the storage container
# Adjust the linked_service_name and folder path to where source data is found
source_dataset = azure_native.datafactory.Dataset(
    name="SourceDataset",
    dataset_name="mysourcedata",
    resource_group_name="myResourceGroup",
    properties=azure_native.datafactory.AzureBlobDataset(
        linked_service_name=linked_service.name,
        folder_path="rawdata/",
        file_name="source_data.csv"
    )
)

# Creation of output dataset defining the destination of transformed data
# Configure the linked_service_name and folder path to where output data is to be stored
sink_dataset = azure_native.datafactory.Dataset(
    name="SinkDataset",
    dataset_name="myoutputdata",
    resource_group_name="myResourceGroup",
    properties=azure_native.datafactory.AzureBlobDataset(
        linked_service_name=linked_service.name,
        folder_path="transformeddata/",
        file_name="output_data.csv"
    )
)

# Define a Dataflow for transforming data
data_flow = azure_native.datafactory.DataFlow(
    name="MyDataFlow",
    data_flow_name="transformdata",
    resource_group_name="myResourceGroup",
    properties=azure_native.datafactory.DataFlowTypeProperties(
        type="MappingDataFlow",
        # A MappingDataFlow requires transformation logic defined here,
        # for the sake of example, we use a placeholder script.
        script="transformation logic goes here",
    )
)

# Define a pipeline with a Dataflow activity
pipeline = azure_native.datafactory.Pipeline(
    name="MyPipeline",
    pipeline_name="etlpipeline",
    resource_group_name="myResourceGroup",
    properties=azure_native.datafactory.PipelineResource(
        # The pipeline references the Dataflow defined above
        activities=[azure_native.datafactory.DataFlowActivity(
            name="TransformActivity",
            data_flow=data_flow.name,
            # Inputs and outputs from the defines datasets
            inputs=[source_dataset.name],
            outputs=[sink_dataset.name]
        )]
    )
)

pulumi.export("data_factory_name", data_factory.name)
pulumi.export("pipeline_name", pipeline.name)
```

This program sets up the necessary resources for an ETL workflow. You will need to customize this with your own transformation logic, connection strings, storage containers, and files. Each of the resources is interconnected, as in a data factory, you need to define linked services, datasets, dataflows, and then construct pipelines that orchestrate the entire process.

Now you can execute `pulumi up` to deploy the infrastructure. After successfully creating the resources, you will be able to see the `data_factory_name` and `pipeline_name` in the outputs. These are the names of the created data factory and pipeline resources that you can then navigate to within the Azure Portal to monitor and manage your ETL workflows.

Remember to safeguard sensitive information such as connection strings and access keys. Always prefer to store such sensitive data in Azure Key Vault and reference them securely within your Data Factory Linked Services.