With a minor update to the Snowflake and DuckDB adapters in dbt, we can now perform complex transformations on Snowflake, save the results as Iceberg tables without any performance hit, and then utilize the more cost-effective DuckDB engine to execute data tests.

• Open Lakehouse Architecture: Adopt an open table format like Apache Iceberg to separate storage from compute, allowing for more flexible and cost-effective use of compute engines.

• DuckDB for Cost Efficiency: Utilize DuckDB for specific workloads to reduce costs significantly compared to Snowflake’s smaller warehouses.

Components Used:

DuckDB, Apache Polaris, Snowflake, Cloud Object Storage, Airflow, DBT

How It Works:

This blueprint orchestrates multi-engine data platform. Data is ingested into a centralized data lake, where it can be accessed and processed by different engines based on specific workload requirements. For example, DuckDB might be used for interactive querying, while Snowflake handles large-scale data processing. The orchestration tool ensures seamless coordination between these components, and the Polaris data catalog enhances discoverability.

Who Would Benefit from This Template:

• Data Engineers looking to build a versatile data infrastructure that can accommodate diverse data workloads.

• Data Scientists requiring different tools and engines for specialized data processing tasks.

• Enterprises aiming to reduce their Snowflake cost while maintaining performance and flexibility.