Leveraging Databricks Delta Lake's optimization capabilities, including Z-Ordering and data skipping, to enhance performance on extensive datasets

Reviewing UDF applications thoroughly and potentially converting them to native Spark SQL functions or rewriting them in a more efficient language such as Scala

Adopting adaptive query execution functionalities in the latest Databricks runtimes to adjust query plans dynamically based on real-time statistics

Examining execution plans via Databricks' Spark UI to spot stages with uneven execution times and applying specific optimizations like broadcast joins or caching