Hi Sam500,
The multi engine pattern usually comes to establishing interoperability at the storage format level while maintaining a single, unified governance layer. You handle this primarily through Unity Catalog (UC) and format flexibility such as Delta with UniForm or native Iceberg.
To get different engines interacting with the same data, you have a few avenues. If you are dealing with engines like OSS Spark, Flink or Trino, the direct approach is leveraging the Iceberg REST Catalog endpoint natively. You can define managed Iceberg tables to allow direct read/write access from those external compute engines.
If you prefer to keep writes optimized in Delta but have downstream consumers on different compute. You can open delta sharing letting external tools read them without requiring data duplication. This is especially practical & clean approach.
If you don't want to move data into the Lakehouse but need to join it with external engines (like Snowflake, Big Query, Postgres or One Lake), you can rely on Lakehouse Federation. UC registers the external metadata and handles live query federation with pushdown optimizations, so the heavy filtering and aggregation happen natively at the source.
The underlying enabler for this is Unity Catalog acting as the universal governance layer. Fine-grained permissions and lineage tracking apply regardless of which compute engine is actually executing the query. The architectures are generally deployed mostly for gradual migrations and specialized processing.
Agentic Lakehouse is the architectural pattern where Generative AI agents are embedded directly into the data platform, treating models, prompts and functions as governed assets along with the tables and pipelines. It means shifting from querying data to deploying agents that can take action on the data using Unity Catalog (UC) as the universal governance layer and using the data for AI/BI actions.
