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In the context of Azure Databricks Lakehouse architecture, data modeling plays a critical role in enabling efficient data processing and analytics. Considering a scenario where an organization is dealing with a high volume of both structured and unstructured data, requiring real-time analytics and the ability to scale dynamically, which of the following statements best describes the importance of data modeling in Lakehouse architecture compared to traditional data warehousing? Choose the best option from the four provided.
In the context of Azure Databricks Lakehouse architecture, data modeling plays a critical role in enabling efficient data processing and analytics. Considering a scenario where an organization is dealing with a high volume of both structured and unstructured data, requiring real-time analytics and the ability to scale dynamically, which of the following statements best describes the importance of data modeling in Lakehouse architecture compared to traditional data warehousing? Choose the best option from the four provided.
Simulated
A
Data modeling is not important in a Lakehouse architecture as it is in a traditional data warehouse, because Lakehouse inherently supports schema-less data storage.
5.5%
B
Data modeling is equally important in both Lakehouse architecture and traditional data warehousing, with identical approaches and methodologies applied in both.
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