Apache Gravitino — 2025 Summary

Source: Dev.to

Introduction

2025 was a landmark year for Apache Gravitino. The project not only graduated as a Top‑Level Project (TLP) but also reached its first major stable release, version 1.0.0. Throughout the year the community focused heavily on “Contextual Engineering” and “AI‑native” metadata management, introducing groundbreaking features such as the Model Context Protocol (MCP) server, the Lance REST service, and a metadata‑driven action system. This article summarizes the milestones and achievements of Apache Gravitino in 2025.

Timeline

• June 3 2025 – Apache Gravitino officially graduated as an Apache Top‑Level Project, marking a significant maturity milestone.
• 2025 – The community released several key versions, including the major 1.0.0 release and feature updates in 0.8.0‑incubating, 0.9.0‑incubating, and 1.1.0.

2025.01.24 – Version 0.8.0‑incubating

• Strengthened AI support with the introduction of the Model Catalog.
• Added credential vending for Filesets and new connectors for Flink (Iceberg/Paimon).

2025.05.07 – Version 0.9.0‑incubating

• Enhanced data governance with a new Data Lineage interface (OpenLineage‑compliant).
• Added gcli script for a better CLI experience.
• Improved security with privilege refinements.

2025.09.24 – Version 1.0.0

• First stable major release, themed “From Metadata Management to Contextual Engineering.”
• Introduced the Metadata‑driven Action System (including Statistics, Policies, and Jobs).
• Launched the MCP (Model Context Protocol) Server, enabling AI agents/LLMs to interact directly with metadata.
• Implemented unified Role‑Based Access Control (RBAC) across catalogs.

2025.11.20 – Version 1.0.1

• Stability release featuring smarter job templates and improved Python client support.

2025.12.19 – Version 1.1.0

• Added the Lance REST service to support vector data for AI workloads.
• Introduced a Generic Lakehouse Catalog and support for Hive 3 and multi‑cluster HDFS Filesets.
• Hardened security for the Iceberg REST service.

Key Features & Improvements

In 2025, Gravitino evolved from a unified catalog to an active metadata control plane. Major technical achievements include:

• AI & LLM Integration – Positioned as an AI‑native catalog by introducing the Model Catalog for managing ML models and the MCP Server to connect AI agents with data context. The Lance REST service (v1.1.0) further solidified support for vector datasets.
• Metadata‑Driven Actions – A new framework allowing users to define policies (e.g., TTL, compaction) and execute jobs based on metadata, moving beyond passive metadata storage.
• Unified Governance & Security – Full implementation of RBAC, credential vending for secure data access (S3/GCS/ADLS), and a unified authentication flow for Iceberg REST services.
• Ecosystem Expansion – New connectors (Generic Lakehouse, Hive 3, Flink, Paimon) and enhancements to the GVFS (Gravitino Virtual File System) for unified file management.

Community

The Apache Gravitino community saw explosive growth in 2025, evolving from an incubator project into a Top‑Level Project backed by a rapidly expanding global ecosystem.

Top‑Level Graduation

• June 3 2025 – Official graduation to an Apache Top‑Level Project, marking maturity in community health, vendor‑neutral governance, and production readiness.

Community Growth (Year‑over‑Year)

• Committer Additions –

  • July 7 2025: Chenxi Pan added as a Committer.
  • December 15 2025: Junda Yang and Yangyang Zhong added as Committers.

• Global Presence – Featured presentations at Community Over Code (NA & Asia) and QCon Shanghai, gathering critical production feedback from worldwide data‑engineering teams to shape the roadmap.

Industry Trends in Metadata Management (2026)

• Breaking Lakehouse Silos – With organizations adopting multiple “open” table formats, “format lock‑in” now supersedes traditional vendor lock‑in. The trend moves toward Universal Lakehouse architectures that provide a single entry point for fragmented data silos.
• The Multimodal AI Explosion – AI workloads are expanding beyond tabular data to massive volumes of unstructured assets (images, video, audio). Traditional data stacks are being replaced by AI‑native multimodal stacks that process complex data types with the same governance as SQL tables.
• Emergence of Data Agents – AI agents are becoming primary data consumers. These agents require Context Engineering—using metadata as an external brain to discover, understand, and act upon data autonomously.
• Escalating AI Security Risks – The high‑speed nature of AI interactions renders static security (RBAC) obsolete. The industry is shifting toward Identity‑Centric Zero Trust and Fine‑Grained ABAC to prevent data leakage and ensure model safety.

Future Work

1. Universal Lakehouse & Format Interoperability

   • Goal: Solve the data‑silo problem by providing a unified management layer for the modern Lakehouse.
   • Multi‑Format Support: First‑class support for Apache Iceberg, Delta Lake, Hudi, and Paimon. Gravitino will act as a “Catalog of Catalogs,” allowing users to manage multiple formats through a single interface, dramatically reducing vendor lock‑in.

2. Multimodal Data Stack for the AI Era

   Gravitino is evolving to empower a new generation of AI‑native data stacks.

   • Ecosystem Integration – Deep integration with AI‑centric engines such as Daft, Ray, and Lance.
   • Empowering New Scenarios – By providing a unified metadata layer for these engines, Gravitino lets users reuse existing data‑governance capabilities—like auditing and access control—for modern multimodal workloads, delivering enterprise‑grade maturity from day one.

3. Data Agent Orchestration (Metadata as the “Brain”)

   Gravitino will serve as the cognitive foundation for autonomous Data Agents.

   • MCP Server & Action System – Leveraging the Model Context Protocol (MCP) and our Metadata Action System, we are exploring scenario‑based capabilities for Data Agents, enabling them to see data and act on it (e.g., schema updates or compaction jobs) using metadata as reasoning context.

4. Advanced Security: KMS & ABAC

   As security threats become more sophisticated in the AI era, Gravitino is implementing more granular and automated controls.

   • ABAC (Attribute‑Based Access Control) – Implement an ABAC engine for fine‑grained permissions based on dynamic tags (e.g., Sensitivity=High) and environmental context.
   • KMS & Credential Management – Integrate with Key Management Services (KMS) to protect data‑at‑rest and in‑transit.

2026 will be a defining year for AI‑native data infrastructure, and the Gravitino community is just getting started.
Whether you’re exploring federated lakehouse architectures, multimodal AI data stacks, or data agents in production, we welcome you to build and evolve Apache Gravitino together with us ❤️.

Gravitino 2025 Summary Blog