Answer-first summary for fast verification
Answer: Create a partition strategy based on the time interval of the data, such as hourly, daily, or monthly partitions, to improve query performance and reduce the amount of data scanned.
Option B is the correct answer because creating a partition strategy based on the time interval of the data, such as hourly, daily, or monthly partitions, allows for efficient querying of data within specific time ranges. This approach is particularly beneficial for time-series data analysis, as it enables faster query execution by reducing the amount of data scanned. Option A is incorrect because partitioning based on the data's size alone may not be sufficient for optimizing query performance. Option C is incorrect because hash-based partitioning does not consider the data's characteristics and may not lead to optimal performance. Option D is incorrect because implementing a partition strategy is essential for optimizing query performance in time-series data scenarios.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
You are tasked with optimizing the performance of a data processing system that handles large volumes of time-series data. The data needs to be partitioned to support efficient data retrieval and analysis. What partitioning strategy would you recommend, and how would you implement it in Azure Data Lake Storage Gen2?
A
Implement a partition strategy based on the data's size, as this is the most important attribute for time-series data performance.
B
Create a partition strategy based on the time interval of the data, such as hourly, daily, or monthly partitions, to improve query performance and reduce the amount of data scanned.
C
Use a hash-based partitioning method to distribute the data evenly across multiple partitions, regardless of the data's characteristics.
D
Do not implement any partition strategy, as the time-series data does not require optimization for query performance.
No comments yet.