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Answer: Employing materialized views to pre-calculate and save complex aggregations
Materialized views are instrumental in boosting query performance by pre-computing and storing intricate aggregations. This approach minimizes the need for recalculating the same aggregations during each query execution, thereby enhancing query response times significantly. Materialized views store query results in a physical table, allowing BI tools to query these results directly. This is particularly beneficial for complex queries involving multiple joins and aggregations, as it substantially reduces the processing required to produce the final outcome. While techniques like denormalizing data, implementing incremental load strategies, and partitioning and clustering tables are also effective, materialized views stand out for their ability to store pre-computed aggregations and complex calculations, making them a key tool for achieving responsive and scalable BI reporting.
Author: LeetQuiz Editorial Team
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When optimizing a Databricks Lakehouse for BI tools, which data modeling and performance optimization techniques would you employ to ensure the BI reporting is both responsive and scalable?
A
Partitioning and clustering tables by frequently queried dimensions to enhance scan efficiency
B
Employing materialized views to pre-calculate and save complex aggregations
C
Adopting incremental load strategies in Delta tables to reduce data movement and refresh durations
D
Flattening data into broad tables to decrease the necessity for real-time joins and aggregations