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Answer: Combine Azure Data Factory and Azure Databricks, using Data Factory for orchestration and Databricks for processing.
Azure Data Factory is a data integration service that allows you to create, schedule, and orchestrate data workflows. It is well-suited for orchestrating data movement and transformation tasks. On the other hand, Azure Databricks is an analytics platform that supports both batch and real-time processing using Apache Spark. By combining Azure Data Factory and Azure Databricks, you can leverage Data Factory for orchestration and scheduling, while using Databricks for the actual data processing tasks. This provides a flexible and powerful solution for handling both batch and real-time data processing needs.
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You are tasked with designing a data pipeline that needs to handle both batch and real-time data processing. Which Azure service would you use and how would you architect the solution?
A
Use Azure Data Factory alone, leveraging its scheduling and pipeline capabilities.
B
Use Azure Databricks alone, leveraging its support for both batch and real-time processing.
C
Combine Azure Data Factory and Azure Databricks, using Data Factory for orchestration and Databricks for processing.
D
Combine Azure Stream Analytics and Azure Databricks, using Stream Analytics for real-time processing and Databricks for batch processing.
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