Analysts filter a 2‑TB Delta table by event_date and customer_id. You see high data‑skipping misses due to out‑of‑order loads. What is the most targeted improvement?
💡 Model Answer
The most effective fix is to apply Z‑order clustering on the filter columns. Z‑ordering reorganizes the file layout so that rows with similar values for event_date and customer_id are stored close together. This dramatically improves data‑skipping because the query engine can prune entire files that do not contain the requested values. Unlike simple partitioning, Z‑ordering works even when the data is loaded out of order, because it reorganizes existing files after the fact. You can run OPTIMIZE <table> ZORDER BY (event_date, customer_id) to rebuild the table. This targeted change reduces I/O, speeds up queries, and eliminates the high data‑skipping misses caused by out‑of‑order loads.
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