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

2026-04-17 · Source: InfoQ · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Advanced, long

Summary

The modern lakehouse architecture, aiming for unified data layers across diverse compute engines like Snowflake, Spark, Trino, and Flink using open standards such as Apache Iceberg, faces a critical interoperability gap: inconsistent SQL dialect for identifier resolution. Each engine and catalog (e.g., Apache Polaris, Databricks Unity Catalog, AWS Glue Data Catalog) applies its own rules for normalizing and resolving identifiers (databases, schemas, tables, columns), leading to "Tower of Babel" effects. This can result in tables being effectively invisible to some engines or requiring pervasive quoting, causing pipeline reliability and consistency issues. For example, Spark might persist `MyTable` as provided, while Flink's case-sensitive lookup for `mytable` fails, or Trino's lowercase normalization prevents discovery of mixed-case tables. This issue, exacerbated in multi-engine lakehouses, necessitates a strategic approach to naming conventions and configuration.

Key takeaway

For AI Architects and Data Engineers designing or managing multi-engine lakehouses, you must treat identifier naming as a critical data contract, not an engine-specific preference. Standardize on a strict, organization-wide lowercase naming convention (e.g., `snake_case`) and configure all engines and catalogs to align. Implement continuous integration (CI) validation to confirm cross-engine discoverability and queryability, mitigating risks of "shadow tables" and pipeline failures.

Key insights

Inconsistent SQL identifier resolution across lakehouse engines and catalogs creates significant interoperability challenges.

Principles

• Shared metadata alone does not guarantee cross-engine portability.
• Identifier normalization is a critical component of data contracts.
• Case-preserving fidelity conflicts with case-normalizing uniformity.

Method

Standardize on a lowercase naming convention with underscores for all identifiers. Configure lakehouse components (Spark, Snowflake CLD, Glue, Trino) to align with this standard. Validate end-to-end portability with CI jobs.

In practice

• Enforce `snake_case` for all table and column names.
• Configure `spark.sql.caseSensitive` to `false` for Spark.
• Implement CI tests to verify cross-engine discoverability.

Topics

• Lakehouse Architecture
• Identifier Resolution
• SQL Interoperability
• Apache Iceberg
• Data Naming Conventions

Code references

• trinodb/trino
• apache/iceberg
• hashicorp/terraform-provider-aws

Best for: AI Architect, Data Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.