A deep dive into query planning, shuffle optimizations, file compaction strategies, and cost-reduction techniques for multi-terabyte Lakehouse pipelines on Databricks.

How to compress transformer and acoustic models for CPU/GPU serving, avoiding PyTorch GIL contention, and achieving ultra-low inference latency under heavy concurrent loads.

Key design patterns for building bulletproof backend services in Python 3.12: handling partial network partitions, managing connection pooling, and implementing structural health checks.