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Answer: Benefits: Enhanced query performance and a more straightforward data model, facilitating quicker data retrieval and analysis. Drawbacks: Higher storage costs due to data redundancy and a risk of inconsistencies in data integrity.
Denormalization is a strategy used to improve read performance by reducing the number of joins needed in queries, which is particularly beneficial in scenarios requiring quick data retrieval and analysis. However, this comes at the cost of increased storage requirements due to data redundancy and potential challenges in maintaining data consistency. Given the project's need for scalable solutions and adherence to compliance standards, the benefits of improved query performance and a simplified data model outweigh the drawbacks, making option A the most suitable choice.
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As a Microsoft Fabric Analytics Engineer Associate working on a data transformation project, you are considering denormalizing your data to optimize performance. The project must adhere to strict compliance standards and requires scalable solutions to handle increasing data volumes. Considering these constraints, what are the potential benefits and drawbacks of denormalization? Choose the option that best fits the scenario. (Select one)
A
Benefits: Enhanced query performance and a more straightforward data model, facilitating quicker data retrieval and analysis. Drawbacks: Higher storage costs due to data redundancy and a risk of inconsistencies in data integrity.
B
Benefits: Lower storage requirements and improved consistency across the dataset. Drawbacks: Degraded query performance and increased complexity in managing the data model.
C
Benefits: Improved security measures and faster access to data. Drawbacks: Complexity in updating data and possible duplication of data entries.
D
Benefits: Streamlined data model and easier updates to data. Drawbacks: Increased need for storage space and slower performance in query execution.