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Why SQE

The Problem

Our data platform runs on Apache Iceberg tables stored in S3, cataloged by Apache Polaris (Iceberg REST Catalog), with authentication through Keycloak OIDC. We need a SQL query engine that can:

  1. Authenticate users through Keycloak
  2. Pass the user’s bearer token through to Polaris and S3 (no service account)
  3. Enforce fine-grained security (row filters, column masks) per user
  4. Support full SQL for analytics, dbt transformations, and ad-hoc queries
  5. Run on Kubernetes with minimal operational overhead

Why Not Trino?

We started with Trino — the industry-standard SQL engine for data lakehouses. It works, but:

ChallengeDetail
Service account modelTrino authenticates to the catalog and storage with a single service identity. It can’t pass per-user bearer tokens through to Polaris. We forked Trino (DCAF branch) to add token passthrough, but maintaining a JVM fork is expensive.
Security enforcementTrino’s security model (system/catalog access control) doesn’t natively support Iceberg-level row filters and column masks applied at the query plan level.
JVM overheadTrino requires significant heap memory, has GC pauses, and takes 10-30 seconds to start. Not ideal for auto-scaling Kubernetes pods.
Maintenance burdenOur Trino fork drifts from upstream with every release. Rebasing is a multi-week effort. Security patches are delayed.

Why DataFusion + Rust?

graph TB
    subgraph "Old: Trino Fork"
        T[Trino JVM] -->|service account| P1[Polaris]
        T -->|service account| S1[S3]
        T -.- Fork[DCAF Fork<br/>maintenance burden]
    end

    subgraph "New: SQE"
        C[SQE Coordinator<br/>Rust binary] -->|user bearer token| P2[Polaris]
        W[SQE Workers] -->|user credentials| S2[S3]
        C --> W
    end

    style Fork fill:#f96,stroke:#333
    style C fill:#6f9,stroke:#333
    style W fill:#6f9,stroke:#333

Apache DataFusion is a Rust-native query engine that gives us:

  • Extensible query planning — we can inject security filters into the LogicalPlan before optimization, which is exactly where row filters and column masks need to go
  • iceberg-rust integration — native Rust Iceberg library, no JNI bridge, no serialization overhead
  • Per-query context — each query gets its own SessionContext with the user’s bearer token. No shared service account.
  • Single binary — the coordinator and worker ship as one ~50MB binary. Starts in milliseconds.
  • No GC — predictable latency, no stop-the-world pauses during large scans

The Name

Sovereign — because every query runs with the identity and permissions of the user who submitted it. No service account intermediary. No privilege escalation. The user’s token is sovereign.