You have terabytes of customer data but your ETL pipelines keep breaking. Your analytics queries take 8 hours to run what should be 20 minutes of work. You've heard about Apache Spark and Kafka but don't know where to start or which problems they actually solve.

Here's the central challenge: big data engineering isn't just about learning tools—it's about understanding which problems require which solutions, and in what order to learn them. This roadmap provides a decision-tree approach to master big data systems from the ground up.

TLDR: Navigate big data engineering through a structured decision tree: start with fundamentals (5 Vs + storage paradigms), choose your architecture pattern (Lambda/Kappa/Medallion), build pipelines (orchestration + processing), then advance to production concerns (dimensional modeling + modern table formats).