About Me I’ve spent over a decade working in real production Big Data systems, primarily in on-prem Hadoop environments. My day-to-day work involves handling issues that most teams struggle with—Spark job failures, performance bottlenecks, memory issues, and inefficient Hive queries. I don’t come from a “course” background—I come from production firefighting, legacy systems, and real deadlines. What I Can Help You With Debugging Spark (Scala / Java / PySpark) job failures Fixing OOM, shuffle, executor, and performance issues Hive query optimization (partitions, joins, file formats) Code refactoring & best practices for Spark jobs Understanding on-prem Hadoop & YARN behavior Guidance for engineers working in legacy Big Data setups Who Should Book a Session Developers stuck with Spark jobs that fail or run too slow Engineers working on on-prem Hadoop clusters Juniors who need help understanding production issues Professionals preparing for Big Data interviews (real-world focus) How Sessions Work One-to-one, focused problem-solving We discuss your specific issue or use case Clear explanation of why the problem occurs Actionable fixes or next steps Screen share if needed This is not a lecture—it’s a working session. Session Format Duration: 45–60 minutes Mode: Online (screen share optional) Confidential & production-safe discussions