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Answer: Implement a pipeline that can dynamically scale based on the data volume and processing requirements.
Ensuring that a data pipeline is scalable and can handle increasing data volumes over time is important for long-term success. Implementing a pipeline that can dynamically scale based on the data volume and processing requirements allows it to adapt to changing conditions and maintain performance as data volumes grow. Designing the pipeline with a fixed set of resources or ignoring its scalability can result in performance issues and limitations as data volumes increase. Focusing only on optimizing the performance for the current data volumes without considering future growth may not be sustainable in the long run.
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You are tasked with designing a data pipeline in Azure Data Factory that extracts data from various sources and loads it into a data lake. How can you ensure that the pipeline is scalable and can handle increasing data volumes over time?
A
Design the pipeline with a fixed set of resources and assume that it will be sufficient for future data volumes.
B
Implement a pipeline that can dynamically scale based on the data volume and processing requirements.
C
Ignore the scalability of the pipeline, as it is not important for extracting and loading data into a data lake.
D
Focus only on optimizing the performance of the pipeline for the current data volumes, without considering future growth.