Article: Lakehouse Tower of Babel: Handling Identifier Resolution Rules Across Database Engines

I've spent enough time debugging cross-engine queries in lakehouses to recognize this as a real operational headache. When you're running Spark, Trino, and DuckDB against the same Iceberg tables, SQL identifier resolution becomes your silent killer—case sensitivity rules, quote handling, and catalog naming conventions differ just enough to create subtle bugs that only surface in production. This isn't a theoretical problem; it's the friction that emerges when engineering teams assume open table formats magically solve integration challenges. The broader trend here matters: as organizations consolidate around lakehouse architectures to reduce data duplication and enable AI workloads, they're inheriting complexity that traditional data warehouses abstracted away. My recommendation? Before standardizing on a multi-engine lakehouse, invest in a thin translation layer or governance framework that normalizes identifier handling. Document your identifier resolution strategy explicitly—treat it like schema governance, not an afterthought. The teams that do this early avoid months of debugging later.