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Answer: Use Azure Data Factory with separate Copy Data activities for each data source type, and then use Azure Data Flow activities to transform the data before loading it into the Synapse Analytics workspace.
Option B is the most suitable approach for ingesting and transforming data from multiple sources into an Azure Synapse Analytics workspace. By using Azure Data Factory, you can leverage separate Copy Data activities for each data source type, ensuring efficient data ingestion. Then, by using Azure Data Flow activities, you can perform the necessary transformations on the data, regardless of the source type. This approach allows for flexible and efficient data processing and seamless integration with the Synapse Analytics workspace.
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You are tasked with designing a solution to ingest and transform data from multiple sources into an Azure Synapse Analytics workspace. The data sources include relational databases, NoSQL databases, and file storage systems. Which of the following Azure services and techniques would you use to achieve this, and how would you design the solution to handle the different data source types?
A
Use Azure Data Factory with a single Copy Data activity to handle all data sources and store the data in Azure Blob Storage. Then, use Azure Synapse Pipelines to load the data into the Synapse Analytics workspace.
B
Use Azure Data Factory with separate Copy Data activities for each data source type, and then use Azure Data Flow activities to transform the data before loading it into the Synapse Analytics workspace.
C
Use Azure Data Lake Storage Gen2 to store the data from all sources, and then use Azure Databricks to run custom scripts for data transformation and loading into the Synapse Analytics workspace.
D
Use Azure Stream Analytics to ingest the data from all sources as a stream, and then use Azure Functions to process the data and store it in the Synapse Analytics workspace.
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