Answer: Partitioning data by time intervals (e.g., hourly or daily) and clustering by key metrics or dimensions

Partitioning data by time intervals and clustering by key metrics or dimensions is the most effective strategy for optimizing query performance in time-series analysis within a Lakehouse environment like Databricks. This method ensures data is organized in alignment with typical time-series queries, enabling the system to swiftly locate and retrieve data for specific time frames, thereby reducing the volume of data scanned during queries. Clustering data by frequently queried metrics or dimensions further enhances performance by minimizing disk reads for these queries. This approach is particularly beneficial for analyses involving sliding time windows, as it facilitates efficient data retrieval and aggregation. Additionally, by reducing the necessity for complex joins, this design significantly boosts query performance, making it ideal for time-series analysis where queries often revolve around time intervals and key metrics.

Author: LeetQuiz Editorial Team