Data Engineering Snowflake dbt Apache Iceberg
Modern Lakehouse Architectures with dbt, Snowflake, and Iceberg
Published on March 15, 2026 by Marcus Vance, Principal Data Architect
The Evolution of the Analytics Platform
Data warehouses have historically forced businesses into proprietary storage standards, escalating compute expenses whenever analysts ran ad-hoc queries.
In this guide, we evaluate the architectural transition from legacy batch ETL systems to an open lakehouse design pattern.
1. Open Table Formats
By adopting open table formats like Apache Iceberg, organizations can:
- Decouple compute from storage entirely.
- Read the exact same data tier using DuckDB, Snowflake, or Databricks without dual-ingestion costs.
- Maintain complete data governance and ACID transactional integrity.
2. Declarative Transformations with dbt
Data pipelines are software. Applying CI/CD testing, documentation, and version control ensures broken source feeds never breach production dashboards.