You have an Azure subscription that contains a storage account named storage1. The storage1 account contains a container named container1. You are planning the development of an app that will…

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Options

• Aa service bus
• Belastic jobs
• Cresources bridges
• DAzure Data Factory

• A
  3% (1)
• B
  13% (5)
• C
  10% (4)
• D
  74% (29)

Explanation

This is a classic Azure data ingestion pattern: use an Azure Storage Account (Blob/Data Lake) as a landing zone, trigger an Azure Function (your app) with a Storage Event Trigger when files arrive, have the Function process data and load it into Azure Synapse Analytics (SQL Pool/Spark), all orchestrated via Azure Data Factory (ADF) for seamless data flow. Here's the breakdown of your architecture and how Azure services fit: Azure Storage Account (Blob/Data Lake Gen2): Acts as the raw data repository (your container) for incoming files from various sources, providing scalable, cost-effective storage. Your App (Azure Function): This custom code (C#, Python, etc.) runs serverlessly. It's triggered by new file events in the container to fetch, transform, and load data. *-> Azure Data Factory (ADF): Orchestrates the whole process, defining the pipeline that includes the trigger and potentially data movement activities. Azure Synapse Analytics Workspace: The destination for processed, structured data, enabling analytics, reporting, and BI. https://learn.microsoft.com/en-us/azure/data-factory/how-to-create-event-trigger

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