I am currently working as Media Analyst with extremely great WLB and around 45k salary. Before joining current organization I was working as Business Intelligence Analyst Intern with absolutely no WLB and 40k salary at the end of which I got diagnosed with a medical condition which forced me to take my current job. Now, that I am fit and doing well. I am stuck with almost a year and half of no coding practise ( which already I was not pretty good at ) and low salary and feeling of not earning to my full potential. I am confused with what to start studying now, Data Science or DevOps. Would appreciate some honest (even if harsh) suggestions

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?

In a world flooded with dashboards and data charts, not all visualizations lead to action. Sometimes, they look good but don’t help decision-makers understand what to do next. That’s why I’m curious, when you create or evaluate data visualizations, what frameworks or methods do you rely on to make sure they’re not just informative, but actually actionable?