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

"Open table formats such as Apache Iceberg standardize data and metadata semantics across engines, but they do not provide SQL dialect interoperability, leaving identifier resolution to each engine."

"In multi-engine lakehouses, identifier resolution has become an architectural concern where a table can exist in shared metadata yet be effectively invisible to some engines or require users to rely on pervasive quoting or escaping."

"Adopting a strict, organization-wide naming convention aligned with the engines and catalogs in the data lakehouse is currently the most reliable way to reduce cross-engine portability failures."

"Teams should treat identifier normalization as part of their data contract, validating and testing naming behavior across engines, rather than assuming that shared metadata alone provides portability."

Modern lakehouse architecture aims for a unified data layer with diverse compute engines like Snowflake and Spark. Despite progress in standardizing data storage and metadata formats, a significant gap exists in SQL dialect interoperability. Each engine has unique rules for resolving identifiers, leading to inconsistencies and challenges in cross-engine portability. To mitigate these issues, organizations should adopt strict naming conventions and treat identifier normalization as part of their data contract, ensuring validation and testing across different engines.

Read at InfoQ