Answer-first summary for fast verification
Answer: Denormalize data into wide tables and leverage Delta Lake's data skipping features on time columns.
To optimize data models for time-sensitive queries in a reporting dashboard, denormalizing data into wide tables is crucial. This approach combines related data from multiple tables into one, reducing the need for joins and enhancing query performance. Wide tables contain all necessary information for report generation, eliminating complex joins. Delta Lake's data skipping features, when applied to time columns, further boost performance by skipping irrelevant data partitions during queries, ensuring faster processing. While storing all data in a single, large Delta table partitioned by time (option D) might seem efficient, it can lead to performance degradation as the table grows. Normalizing data across multiple tables (option A) increases query complexity and may slow down performance due to necessary joins. Row-level security for time-based data filtering (option B) can improve query load times but isn't as effective as denormalization and data skipping for optimizing time-sensitive queries.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
How would you model your data in Delta Lake to ensure fast and efficient queries for a reporting dashboard that handles time-sensitive data?
A
Normalize data across multiple Delta tables to minimize redundancy and save on storage costs.
B
Implement row-level security to filter data by time, thereby reducing query load times.
C
Denormalize data into wide tables and leverage Delta Lake's data skipping features on time columns.
D
Store all data in a single, large Delta table partitioned by time, such as by the hour of the day.
No comments yet.