Let me ask, when you are designing a Delta Lake table, how do you think about partitioning and file optimization?

When designing a Delta Lake table, I first analyze query patterns to determine the most selective columns for partitioning. Common choices are date, region, or user ID, but I avoid high‑cardinality columns that would create too many small partitions. I also consider the size of each partition; aiming for 1–10 GB per partition balances read performance and metadata overhead. For file optimization, I use Delta Lake’s OPTIMIZE command to compact small files into larger ones (ideally 1–4 GB) and apply Z‑order clustering on frequently filtered columns to co‑locate related data on disk. This reduces the amount of data scanned during queries. I also enable auto‑compaction in the job pipeline to keep the file layout healthy over time. By aligning partitioning with query predicates and regularly optimizing file sizes, I ensure fast, cost‑effective reads.