How can you insert your API Query Manager data in Microsoft Azure
Import your data in Microsoft Azure with our API QM product
With Dataslayer you can pull a URL directly from our API QM product in JSON format (learn how here). Each URL corresponds to a data table.
To enter the information of each URL that you generate with us to Azure, it is as easy as having a lambda function configured that calls as many URLs as you generate and need, convert the JSON format to CSV if you wish, and finally upload it to your Azure account. All this can be done by the lambda function.
This is an example of a lambda function that calls different URLs made with our API QM product to push that URL data into Azure. In this version of the Lambda function, we use the azure.storage.blob module to interact with Azure Storage. To authenticate the connection, you'll need to get a connection string and container name from your Azure Storage account and provide them in the function. Additionally, we use a BlobClient to interact with the Blob objects. This example is made in Python, but it can be transformed into any programming language you want, and you can also take advantage of lambda to transform the data when saving it to your Azure account:
import requests from azure.storage.blob import BlobServiceClient def lambda_handler(event, context): urls = [ # Each API Query Manager URL # ... ] # Retrieve the connection string and container name from environment variables connect_str = '<connection_string>' container_name = '<container_name>' # Create a BlobServiceClient object to interact with the Blob storage account blob_service_client = BlobServiceClient.from_connection_string(connect_str) for url in urls: response = requests.get(url) data = response.json() # Get a BlobClient object to represent the blob blob_client = blob_service_client.get_blob_client(container=container_name, blob='<file_name>.json') # Upload the data to the blob blob_client.upload_blob(data) return 'Data stored in Azure Blob storage successfully'
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Updated on: 23/08/2023