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Answer: Use the 'optimize' command with the 'ZORDER BY' clause to reorganize and compact the table files, improving read performance by colocating related data.
The 'optimize' command, especially when used with the 'ZORDER BY' clause, is the most effective solution for this scenario. It not only consolidates small files into larger ones to reduce the overhead of file operations but also optimizes the data layout for better query performance by colocating related data. Increasing the batch size (A) might reduce the number of read calls but does not address the root cause of the performance issue. Implementing a custom filtering function (C) could reduce the amount of data processed but adds complexity and may not be feasible for all scenarios. Manually merging files (D) is error-prone and not scalable or maintainable for large datasets. Therefore, option B is the best choice as it directly addresses the performance issues while ensuring long-term maintainability and cost-effectiveness.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with optimizing the performance of read operations on a Delta table in Azure Databricks. The table currently suffers from performance issues due to the presence of a large number of small files, which is causing excessive overhead during read operations. Your solution must not only address the immediate performance issues but also consider long-term maintainability and cost-effectiveness. Which of the following approaches would you choose to optimize the reads from the Delta table? (Choose one option)
A
Increase the batch size of the read operations to reduce the number of read calls.
B
Use the 'optimize' command with the 'ZORDER BY' clause to reorganize and compact the table files, improving read performance by colocating related data.
C
Implement a custom filtering function within the read operation to skip unnecessary data, thereby reducing the amount of data processed.
D
Manually merge the small files into larger files using an external tool, then update the Delta table metadata to reflect the new file structure.
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