Incremental Data Loading for Real-Time Model Updates with Azure Data Factory

Question

Pulumi · Accepted Answer

Incremental data loading is a pattern used to load only new or changed data within a data source, as opposed to consistently reloading the entire data set. It is especially effective for real-time or near-real-time data processing, where models or reports are updated frequently with the latest data.

To implement incremental data loading in Azure, we use Azure Data Factory (ADF), a cloud-based data integration service that allows you to create, schedule, and orchestrate your data pipelines at scale.

Below is a Pulumi program in Python that outlines the steps required to set up an Azure Data Factory, create a pipeline with a copy activity for incremental data loading, and set a trigger to automate the pipeline execution.

Here is what the code does:
1. Creates an Azure Data Factory instance.
2. Sets up a pipeline that outlines the steps the data will go through, such as copying from the source data store to the destination data store.
3. Configures a trigger that determines when the pipeline should execute – this could be on a schedule or when a particular event occurs (for example, new data arrives).

```python
import pulumi
import pulumi_azure_native as azure_native

# Ensure that the required Azure Native version is available.
pulumi_azure_version = azure_native.__version__
assert pulumi_azure_version == "2.11.0", f"Required version is 2.11.0, your version is {pulumi_azure_version}"

# Replace these with appropriate values
RESOURCE_GROUP_NAME = "my-resource-group"
DATA_FACTORY_NAME = "my-data-factory"
PIPELINE_NAME = "my-incremental-loading-pipeline"
TRIGGER_NAME = "my-tumbling-window-trigger"

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup(RESOURCE_GROUP_NAME)

# Create an Azure Data Factory
data_factory = azure_native.datafactory.Factory(
    f"{DATA_FACTORY_NAME}",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    # Additional Factory settings can be configured here
)

# Define the pipeline for data loading
pipeline = azure_native.datafactory.Pipeline(
    PIPELINE_NAME,
    resource_group_name=resource_group.name,
    factory_name=data_factory.name,
    activities=[{
        "name": "CopyActivity",
        "type": "Copy",
        # Here you would define your source and sink datasets, as well as the specifics of your copy activity,
        # like the type of copy behavior (e.g., incremental) and the high watermark to use for identifying new data.
    }],
    # Additional pipeline settings like parameters, concurrency, descriptions can be added here.
)

# Define a trigger for the pipeline
trigger = azure_native.datafactory.Trigger(
    TRIGGER_NAME,
    resource_group_name=resource_group.name,
    factory_name=data_factory.name,
    properties={
        # Define your trigger properties here, for example, a TumblingWindowTrigger that executes
        # every X minutes and passes parameters to the pipeline for the incremental load.
    },
    # Additional trigger settings can be configured here.
)

# To enable manual invocation of the pipeline, use the following snippet 
# (commented-out, because we already have the trigger above)
# manual_trigger = azure_native.datafactory.CreateRunRequestArgs(
#     factory_name=data_factory.name,
#     pipeline_name=pipeline.name,
#     resource_group_name=resource_group.name,
#     # Parameters for the pipeline run can be passed here if necessary.
# )

# Finally, we export the name of the data factory and the pipeline
pulumi.export("data_factory_name", data_factory.name)
pulumi.export("pipeline_name", pipeline.name)
pulumi.export("trigger_name", trigger.name)
```

This program is a starting point. It does not include detailed configurations for datasets, linked services, or the specific logic to determine which data to copy for your incremental load. Each of these would be highly dependent on your specific data sources, the formats they use, and how they indicate new or updated data.

Before running this Pulumi program, ensure that you have configured your Azure credentials on the machine where you intend to run Pulumi. You can do that by logging in with the Azure CLI using `az login`, and Pulumi will use those credentials by default.

Deploying this infrastructure is done by running `pulumi up` in the directory where this script is located after installing Pulumi and creating a new Pulumi project. It will prompt you to confirm the actions before anything is created in Azure.

For a production scenario, you would need to add details about the source and sink datasets, such as file paths, table names, and any filters or transformations that are applied as part of the copy activity. You may also want to manage your trigger more dynamically, perhaps checking for new data files at regular intervals or in response to an event, like the arrival of new data in a storage container.