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Azure AI form intelligence is a wonderful way of reading image invoices. You can now create a document intelligence API and pass on an image document, with preseiely reading embdded words, figures and everything. They return contents in both literature as well as JSON based contents. And guess what? Whenever there is a JSON based content involved, obvously you can leverage them on Azure middlewares like Azure Logic Apps, can't you? Reading such a JSON response could be tricky. And this document can give you a fair idea to achieve this. And here are the steps: Step 1:  Create a Document intelligence API: Go to https://portal.azure.com and create a new Form recognizer resource: For our demo purpose we are selecting 'Standard' pricing tier. Alternately you can also select Free tier, if needed. Click 'Preview + create' >> Create finish the wizard. It will take few moments to create the resource. Come to the resource thus created and note the endpoint and access ke

    ADF is a very convinient way of moving data from external system to Azure SQL storages (Azure SQL, Azure MySQL, Azure cosmos DB, Cassandra: not to mention blob, table and file systems), without writing any code. In fact dataflows are so convinent ways of handling data, that it definitely does have an upperhand as compared to Azure Synapses, where you need to write a lot of staff in Python on JuPyter Notebook to instigate the cleaning and transformation processes. However your ADF dataflows could fail very weirdly, if you have not considered these options. These happen typically when any of your Source/sink involves an Azure SQL connection: A. Missing property error: Your piepline gave a nasty error as:  What?!!! Wait -- I have validated the pipeline and published everything without any errors. Even then, why on earth, did it happen? Solution : While creating the sink/source Linked Service, you need to select 'Legacy' instead of 'Recommended' in  Version  parameter:

¡Hola! I am sure you must have gone through my earlier post that housed an introduction to Azure Bicep and how we can easily manage mass deployment of resources, dynamically using the same: https://subsd365.blogspot.com/2024/05/manage-your-azure-resource-deployments.html This blog itself gave you an insight of deploying Azure resources and resource groups using Azure biceps. However in my next blog, I will take a deeper look at Azure bicep, giving you a feel of creating and managing and deploying a bit more complex resources which have dependencies and a lot of overheads to consider. One of the shortfalls of the ARM template was: your ARM template is always needed to be tagged to a subscription or a resource group. Consequently it's very difficult to create a resource group and then add resources to that resource group. What you used to do as a workaround was: to create two independent templates: one for resource group and then another for resources, and then execute them sequent