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Answer: Partition the Delta table by the timestamp column to improve query performance and enable efficient data pruning based on time ranges.
Partitioning the Delta table by the timestamp column is the most effective strategy for optimizing query performance on time-series data. It allows the query engine to skip irrelevant data partitions, significantly reducing query times and costs. This approach also supports scalability and complies with data retention policies by enabling easy data pruning. While Delta Lake's time travel feature is useful for accessing historical data, it does not optimize query performance or data management efficiency on its own. Disabling the transaction log compromises data integrity and recovery capabilities, and using the upsert feature without partitioning does not address the core need for efficient query performance.
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In the context of managing a large-scale data pipeline that processes time-series data stored in a Delta table, you are tasked with optimizing both the performance of data queries and the efficiency of data management. The solution must adhere to cost constraints, ensure compliance with data retention policies, and support scalability as data volume grows. Considering these requirements, which of the following strategies is the BEST approach to efficiently manage and query this time-series data using Delta Lake's features? Choose the single best option.
A
Partition the Delta table by the timestamp column to improve query performance and enable efficient data pruning based on time ranges.
B
Utilize Delta Lake's time travel feature to access historical data for compliance audits, without partitioning the table.
C
Disable the transaction log to minimize storage costs and reduce the overhead associated with logging changes to time-series data.
D
Implement Delta Lake's upsert feature for time-series data updates, ignoring the benefits of partitioning for query performance.
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