You mentioned using the Spark UI to identify slow stages and adjust parallelism. How would you ensure ACID transactions, schema evolution, and good query performance in your Spark workloads?

To guarantee ACID semantics in Spark, I use Delta Lake, which adds a transaction log and snapshot isolation to the Parquet format. Delta automatically handles concurrent writes, compaction, and schema enforcement. For schema evolution, Delta supports adding columns, changing types, and dropping columns with the mergeSchema option; I enable it only for append‑only workloads to avoid accidental data corruption. Query performance is improved by partitioning on high‑cardinality, time‑based columns, using Z‑Ordering for hot columns, and caching frequently accessed tables. I also leverage broadcast joins for small dimension tables, enable Tungsten memory management, and tune the shuffle partitions (spark.sql.shuffle.partitions) based on cluster size. Finally, I monitor the Spark UI and the Delta Lake metrics to spot slow stages, and I adjust spark.default.parallelism or spark.sql.shuffle.partitions accordingly, ensuring that the job remains within the desired latency window while preserving ACID guarantees.