> For the complete documentation index, see [llms.txt](https://docs.e6data.com/query-engine/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.e6data.com/query-engine/get-started/architecture/detailed-architecture-walkthrough/how-the-architecture-compares.md).

# How the architecture compares

e6data combines the compute–storage separation of cloud warehouses with the open formats of lakehouse platforms, and adds a compute plane that is stateless, granular, and protocol-compatible with existing tools.

| Architectural dimension   | e6data                                                                                               | Typical cloud data warehouse                         | Typical lakehouse platform                          |
| ------------------------- | ---------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | --------------------------------------------------- |
| **Where your data lives** | In your lake, in place — queried directly, never ingested                                            | Ingested into vendor-managed storage                 | In your lake, alongside platform-managed workspaces |
| **Storage format**        | Open only: Iceberg, Delta Lake, Hudi, Parquet                                                        | Proprietary internal format; open formats layered on | One favored open format; others via interop layers  |
| **Dialects & protocols**  | Transpiles other engines' dialects to E6 SQL; speaks Postgres wire, Trino, Spark, gRPC               | Own SQL dialect; vendor drivers required             | Own dialect; clients use platform drivers           |
| **Compute model**         | Independently scalable planner and executor tiers — no driver bottleneck                             | Fixed-size virtual warehouses                        | Driver-and-worker clusters and serverless pools     |
| **Scaling granularity**   | Atomic — add one planner or one executor at a time, on load and query complexity                     | Step jumps — whole-warehouse resize or multiply      | Node-level within a cluster                         |
| **Execution engine**      | Vectorized columnar engine, Rust-based and Arrow-centric                                             | Proprietary vectorized engine                        | Vectorized engine over a JVM-based runtime          |
| **Catalog & governance**  | Attaches to your existing catalogs and governance — Hive Metastore, Glue, Unity, Polaris/IRC, Ranger | Vendor's own catalog at the center                   | Platform's own catalog at the center                |

Category columns describe common architecture patterns, not any specific product, and are intended as an architectural orientation rather than a feature matrix.


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