3. Dataset Binding with Azure

We will create an Azure Function App to continuously sync a blob with Dataloop’s dataset

If you want to catch events from the Azure blob and update the Dataloop Dataset you need to set up a blob function.The function will catch the blob storage events and will reflect them into the Dataloop Platform.

If you are familiar with Azure Function App, you can just use our integration function below.

We assume you already have an Azure account with resource group and storage account. If you don’t, follow the Azure docs and create them.

3.1. Create the Blob Function

  1. Create a Container in the created Storage account

    • Public access level -> Container OR BlobNOTE this container should be used as the external storage for the Dataloop dataset.

  2. Go back to Resource group and click Create -> Function App

    • Choose Subscription, your Resource group, Name and Region

    • Publish -> Code

    • Runtime stack -> Python

    • Version -> <=3.7

In VScode, flow the instructions in azure docs to configure your environment and deploy the function:

  1. Configure your environment

  2. Sign in to Azure

  3. Create your local project

    • in Select a template for your project’s first function choose -> Azure Blob Storage trigger

    • in Storage account select your Storage account

    • in Resource group select your Resource group

    • Set the ‘Create new Azure Blob Storage trigger’ to your container name (used in the Dataloop platform)assets/bind_azure/trigger_dataset.pngadd_layer

    • open the code file

    • add dtlpy to the requirements.txt file

    • add “disabled”: false to the function.json file

    • add a function code to __init__.py file

import azure.functions as func
import dtlpy as dl
import os
os.environ["DATALOOP_PATH"] = "/tmp"
dataset_id = os.environ.get('DATASET_ID')
dtlpy_username = os.environ.get('DTLPY_USERNAME')
dtlpy_password = os.environ.get('DTLPY_PASSWORD')
def main(myblob: func.InputStream):
    dl.login_m2m(email=dtlpy_username, password=dtlpy_password)
    dataset = dl.datasets.get(dataset_id=dataset_id,
                              fetch=False  # to avoid GET the dataset each time
    # remove th Container name from the path
    path_parser = myblob.name.split('/')
    file_name = '/'.join(path_parser[1:])
    file_name = 'external://' + file_name
  1. Deploy the code to the function app you created.

  2. In VS code go to view tab -> Command Palette -> Azure Functions: Upload Local Settings

  3. Go to the Function App -> Select your function -> Configuration (Under Settings section)* add the 3 secrets vars DATASET_ID, DTLPY_USERNAME, DTLPY_PASSWORD

Done! Now your storage blob will be synced with the Dataloop dataset