Explanation:
Using Azure Data Factory to create a pipeline with multiple activities for data integration and transformation using Azure Databricks provides a scalable and efficient solution. Monitoring using Azure Monitor ensures that the pipeline performance and health are continuously tracked.
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You are tasked with creating a data pipeline in Azure Data Factory to process a daily batch of transactional data. The pipeline must handle data from multiple sources, perform transformations, and load the data into a data warehouse. Describe the steps you would take to design and implement this pipeline, including data integration, transformation, and monitoring.
A
Use Azure Data Factory to create a pipeline with multiple activities for data integration, transformation using Azure Databricks, and monitoring using Azure Monitor.
B
Use Azure Data Factory to create a pipeline with multiple activities for data integration, transformation using Azure Functions, and monitoring using Azure Monitor.
C
Use Azure Data Factory to create a pipeline with multiple activities for data integration, transformation using Azure Databricks, and monitoring using Azure Log Analytics.
D
Use Azure Data Factory to create a pipeline with multiple activities for data integration, transformation using Azure Functions, and monitoring using Azure Log Analytics.