When working with petabyte-scale datasets using distributed frameworks like Hadoop or Spark, what strategies, configurations, or code-level optimizations do you apply to reduce processing time and resource usage? Any key lessons from handling performance bottlenecks or data skew?

I have tried my best to collect data from surveys, questionnaire, interviews and group discussions. What else can be my choice? I follow the above model. Please suggest a better framework to better represent the collected data.