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Answer: Benefits: Enables more detailed insights and advanced analytics by increasing data granularity. Drawbacks: Leads to higher storage costs and may introduce data redundancy and inconsistencies.
De-aggregation is a process that breaks down aggregated data into finer, more detailed pieces, which enhances the ability to perform in-depth analysis and gain detailed insights. However, this increased granularity comes at the cost of higher storage requirements and the potential for data redundancy and inconsistencies, as the same data might be stored in multiple forms. The correct answer highlights these key trade-offs, making it the most accurate choice for understanding the implications of de-aggregation in a real-world scenario.
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As a Microsoft Fabric Analytics Engineer Associate working on a project to implement a data de-aggregation process, you are tasked with evaluating the potential impacts of this approach. Considering the need for detailed insights for advanced analytics, while also being mindful of storage costs and data integrity, which of the following best describes the benefits and drawbacks of de-aggregation? Choose the single best option.
A
Benefits: Enables more detailed insights and advanced analytics by increasing data granularity. Drawbacks: Leads to higher storage costs and may introduce data redundancy and inconsistencies.
B
Benefits: Reduces storage costs and ensures data consistency by minimizing data granularity. Drawbacks: Limits the depth of insights and analytics capabilities due to reduced data detail.
C
Benefits: Enhances data security and speeds up data access times. Drawbacks: Complicates data update processes and risks data duplication.
D
Benefits: Streamlines the data model and simplifies data updates. Drawbacks: Increases storage needs and may degrade query performance.
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