1. Data Movement for Distributed Model Training with Azure Data Factory


    In order to move data for distributed model training with Azure Data Factory (ADF), you would need to create an ADF instance, define linked services to connect to data sources and destinations, create datasets representing your data, and use pipelines to orchestrate and automate data movement and transformation tasks.

    Here is a detailed process of what is required to set up data movement for distributed model training with Azure Data Factory using Pulumi in Python:

    1. Create an Azure Data Factory: This is the main orchestrator for data movement and transformation, which we will create using the azure-native.datafactory.Factory resource.

    2. Create Linked Services: These are connections to data stores like Azure Blob Storage, Azure SQL Database, or any other supported data sources. They will be used to link your data storage with the Data Factory. You need to create a linked service for each data source or sink using azure-native.datafactory.LinkedService.

    3. Create Datasets: These are representations of your data in the source and where you want to land the data in the sink. For example, you would define a dataset for the source data in Azure Blob Storage and another for the sink, like Azure SQL Database, using azure-native.datafactory.Dataset.

    4. Create Pipelines and Activities: A pipeline is a logical grouping of activities that perform a unit of work. Activities within a pipeline might translate to copying data, running a Hive query, executing a stored procedure, etc. You will define your data movement and transformation logic inside a pipeline using azure-native.datafactory.Pipeline.

    5. Create Triggers: Triggers are responsible for the initiation of the pipeline executions. You can have a schedule trigger for time-based execution or event-based triggers when a file arrives or is updated in a blob storage, for example, using azure-native.datafactory.Trigger.

    Here's a Pulumi program in Python that would set up an Azure Data Factory and the necessary components for data movement:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group for Data Factory and other resources resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure Data Factory instance data_factory = azure_native.datafactory.Factory("data_factory", resource_group_name=resource_group.name, location=resource_group.location, ) # Define Azure Blob Storage Linked Service (as an example of a data source) blob_storage_linked_service = azure_native.datafactory.LinkedService("blob_storage_linked_service", resource_group_name=resource_group.name, factory_name=data_factory.name, properties=azure_native.datafactory.BlobStorageLinkedServiceTypePropertiesArgs( url="https://<account_name>.blob.core.windows.net/<container_name>", connection_string="<connection_string>", ) ) # Define Dataset for source data source_dataset = azure_native.datafactory.Dataset("source_dataset", resource_group_name=resource_group.name, factory_name=data_factory.name, properties=azure_native.datafactory.AzureBlobDatasetTypePropertiesArgs( folder_path="input-data/", linked_service_name=blob_storage_linked_service.name, ) ) # Define Dataset for destination (sink) sink_dataset = azure_native.datafactory.Dataset("sink_dataset", resource_group_name=resource_group.name, factory_name=data_factory.name, properties=azure_native.datafactory.AzureSqlTableDatasetTypePropertiesArgs( table_name="dbo.TrainingData", linked_service_name=<linked_service_for_sql_database>.name, ) ) # Define a Pipeline for data movement data_movement_pipeline = azure_native.datafactory.Pipeline("data_movement_pipeline", resource_group_name=resource_group.name, factory_name=data_factory.name, activities=[azure_native.datafactory.CopyActivityArgs( name="Copy_from_blob_to_sql", inputs=[azure_native.datafactory.DatasetReferenceArgs( reference_name=source_dataset.name )], outputs=[azure_native.datafactory.DatasetReferenceArgs( reference_name=sink_dataset.name )], source=azure_native.datafactory.BlobSourceArgs(), sink=azure_native.datafactory.SqlSinkArgs(), )] ) # Define a Trigger for the Pipeline pipeline_trigger = azure_native.datafactory.Trigger("pipeline_trigger", resource_group_name=resource_group.name, factory_name=data_factory.name, properties=azure_native.datafactory.BlobEventsTriggerArgs( events=["Microsoft.Storage.BlobCreated", "Microsoft.Storage.BlobDeleted"], blob_path_begins_with="input-data/", blob_path_ends_with="", scope=f"/subscriptions/{<your_subscription_id>}/resourceGroups/{resource_group.name}/providers/Microsoft.Storage/storageAccounts/{<your_blob_storage_name>}/blobServices/default/containers/{<your_container_name>}", ) ) # Export the Data Factory URL for easy access pulumi.export('data_factory_url', pulumi.Output.concat('https://adf.azure.com/factories/', data_factory.name))

    This program defines the necessary resources to set up a data movement process with Azure Data Factory:

    • A Resource Group to hold all our components.
    • The Data Factory instance itself.
    • Linked Services to connect the Data Factory to our data stores.
    • Datasets that represent the structure and location of the source and target data.
    • A Pipeline that defines the data movement tasks, in this case, a simple copy activity from blob storage to a SQL table.
    • A Trigger to start the pipeline run based on blob storage events.

    Make sure to replace placeholders with your actual Azure storage account name, container name, blob path, connection strings, and any other necessary details. After deploying this Pulumi program, your Azure Data Factory will be ready to orchestrate the specified data movement for distributed model training.