How Gravitino, Daft, and Lance Enable Secure, AI‑Driven Multimodal Lakehouse

01 Introduction: New Challenges for Multimodal Lakehouses

With the explosive growth of multimodal data and AI applications, traditional data‑centric architectures that focus on structured data face unprecedented difficulties. Heterogeneous data such as text, images, video, and vectors are scattered across Iceberg, Hudi, object stores, and vector databases, creating "metadata islands" that hinder a unified view and exacerbate stack inconsistency.

Existing stacks also suffer from low computational efficiency—Spark lacks native UDFs for multimodal data and Python frameworks struggle to scale—and storage bottlenecks, where columnar formats like Parquet incur Row Group overhead and I/O amplification in high‑frequency random‑access and vector‑search workloads.

02 Gravitino: AI‑First Unified Metadata Catalog

Gravitino is not merely a metadata manager; it is a "catalog of catalogs" designed for the AI era. It offers a unified data view that bridges heterogeneous sources, including lake tables (Iceberg/Hudi), unstructured filesets, feature data, model metadata, and Lance vector data.

1. Unified View and Federated Query

Through a unified REST API, Gravitino enables federated queries that combine traditional Hive tables with Lance multimodal AI data in a single query, breaking the BI‑AI data boundary.

2. Metadata‑Driven Actions

TTL (Time‑To‑Live): automatic cleanup of expired data.

Compaction: merge small files to improve storage and query performance.

Data migration and compression: move or compress data based on hot‑cold patterns to lower‑cost storage tiers.

These capabilities turn governance into proactive optimization rather than reactive response.

03 Lance Namespace: SPEC‑First Open Ecosystem Philosophy

The Lance community adopts a SPEC‑first approach for its Namespace metadata layer, mirroring Iceberg’s success. By defining a language‑agnostic specification, Lance encourages implementations in Rust, Java, Python, and integration with engines such as Spark, Daft, and Trino, avoiding ecosystem lock‑in.

04 Two Integration Paths Between Gravitino and Lance

Gravitino provides two complementary ways to integrate with Lance, each targeting different scenarios.

Path 1: Gravitino Table API

Core advantage: All Lance operations go through Gravitino’s REST API, gaining unified view, federated query, and enterprise‑grade governance (access control, audit, lineage, optimization).

Applicable scenarios: Federated analysis across Lance and other sources, or environments requiring standardized, enterprise‑level security for all assets.

Path 2: Gravitino as Lance Catalog

Core advantage: Existing Lance users can point the native Lance client’s catalog to Gravitino, preserving Lance‑specific features like Time Travel while adding Gravitino’s metadata persistence and management.

Applicable scenarios: Applications built on Lance’s native API that need external metadata management without code changes.

05 Enterprise‑Grade Security Governance for Multimodal Data

Gravitino delivers a low‑cost, enterprise‑level security framework covering authentication, authorization, and audit for multimodal sources such as Lance.

Authentication Mechanisms

OAuth2 – seamless integration with modern cloud services.

Kerberos – compatibility with existing big‑data authentication ecosystems.

Simple – username‑only authentication for development or low‑security contexts.

Extensible plugins – custom authentication logic for special requirements.

Fine‑Grained RBAC

Gravitino’s RBAC model introduces two notable designs:

Privilege Inheritance: Objects are organized as a tree Catalog -> Schema -> Table. Granting a permission at a higher level (e.g., Schema) automatically applies to all descendant tables, simplifying bulk authorization.

Deny‑First rule: Explicit Deny entries override inherited allowances, allowing administrators to block access to sensitive tables even when broader permissions exist.

This combination offers financial‑grade security rigor while remaining operationally efficient.

06 Summary and Future Outlook

The "three‑piece set" of Gravitino, Daft, and Lance addresses multimodal lakehouse challenges by providing open‑source, collaborative, and high‑performance solutions:

Gravitino delivers a unified view and strong governance, eliminating metadata islands.

Daft offers distributed compute tailored for multimodal workloads.

Lance solves storage bottlenecks, especially for random access and vector retrieval.

This combination avoids the cost and complexity of stitching together disparate single‑function systems and mitigates vendor lock‑in. Looking ahead, the Gravitino community plans deeper integration with Daft and Lance, broader adoption of Lance REST Namespace in production, and continued co‑evolution of an AI/BI‑integrated multimodal lakehouse paradigm.