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Answer: Use Azure Data Factory with a Copy Data activity to ingest the JSON data from the REST API and store it in Azure Blob Storage. Then, use a Data Flow activity to flatten the JSON data and store it in Azure Data Lake Storage Gen2.
Option A is the most suitable approach for ingesting and transforming JSON data from a REST API into Azure Data Lake Storage Gen2. By using Azure Data Factory, you can leverage the Copy Data activity to ingest the JSON data from the REST API and store it in Azure Blob Storage. Then, by using a Data Flow activity, you can flatten the nested JSON structures and store the transformed data in Azure Data Lake Storage Gen2. This approach allows for efficient data ingestion, transformation, and storage without the need for custom scripts or external services.
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You are tasked with designing a solution to ingest and transform data from a REST API into an Azure Data Lake Storage Gen2. The data from the REST API is in JSON format and contains nested structures. Which of the following Azure services and techniques would you use to achieve this, and how would you design the solution to handle the nested JSON data?
A
Use Azure Data Factory with a Copy Data activity to ingest the JSON data from the REST API and store it in Azure Blob Storage. Then, use a Data Flow activity to flatten the JSON data and store it in Azure Data Lake Storage Gen2.
B
Use Azure Event Hubs to capture the JSON data from the REST API, and then use Azure Stream Analytics to process the data and store it in Azure Data Lake Storage Gen2.
C
Use Azure Logic Apps to trigger a workflow for each JSON object in the REST API, and then use a custom connector to flatten the JSON data and store it in Azure Data Lake Storage Gen2.
D
Use Azure Databricks to run a custom script that ingests the JSON data from the REST API, flattens the nested structures, and stores the transformed data in Azure Data Lake Storage Gen2.
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