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Answer: Partition by device_id, z-order by timestamp, apply bloom filters on reading_type.
Partitioning by device_id allows for efficient querying of data from specific devices. Z-ordering by timestamp helps in clustering related data together, which is beneficial for time-series analysis. Bloom filters on reading_type improve the speed of lookups for specific types of readings.
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Given a dataset of sensor readings from IoT devices, which includes columns like device_id, reading_type, timestamp, and value. Describe how you would apply Delta Lake optimizations such as partitioning, z-ordering, and bloom filters to this dataset to enhance query performance. Consider the typical query patterns and the size of the dataset.
A
Partition by device_id, z-order by timestamp, apply bloom filters on reading_type.
B
Partition by reading_type, z-order by device_id, apply bloom filters on timestamp.
C
Partition by timestamp, z-order by reading_type, apply bloom filters on device_id.
D
Partition by value, z-order by timestamp, apply bloom filters on device_id.
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