Databricks vs. BigQuery for unified Batch/Streaming code reuse (Data Science focus)?

Hey everyone, Comparing Databricks vs. BigQuery for a new architecture. Our primary requirement: writing complex Python feature engineering/ML logic once for historical data (batch), and deploying that exact same code into a real-time stream without rewriting it. Our team is primarily Data Scientists and Python-first engineers , so avoiding "logic drift" between offline model development and live production scoring is critical. From my research: Databricks: Unified Spark API lets us change the trigger (like using Real-Time Mode) to run complex Python, ML models, and stateful logic at milliseco