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How should you design your data model in Databricks Lakehouse for optimal query performance in time-series analysis, especially for aggregations and analytics over sliding time windows?
A
Normalizing data into multiple related tables to reduce redundancy and storage requirements
B
Storing raw event data in blob storage and using Delta Lake only for aggregated summaries
C
Partitioning data by time intervals (e.g., hourly or daily) and clustering by key metrics or dimensions
D
Structuring data in a flat wide table format to minimize the need for joins
