Apache Gravitino Introduction
Source: Dev.to
Author: shaofeng shi
Last Updated: 2025‑12‑29
Overview
In the era of big data, enterprises often need to manage metadata from multi‑cloud, multi‑domain, and heterogeneous data sources (e.g., Apache Hive, MySQL, PostgreSQL, Iceberg, Lance, S3, GCS, etc.). With the rapid adoption of AI model training and inference, massive multimodal data and model metadata also require a unified management solution.
Traditional approaches manage metadata separately for each source, which increases operational complexity and creates data silos. Apache Gravitino—a high‑performance, geographically distributed federated metadata lake—offers a unified solution for managing multi‑source metadata.
Project History
| Milestone | Date |
|---|---|
| Initiated & founded by Datastrato Inc. | – |
| Open‑sourced | 2023 |
| Donated to Apache Incubator | 2024 |
| Graduated to Apache Top‑Level Project | May 2025 |
Deployed in production at companies such as Xiaomi, Tencent, Zhihu, Uber, and Pinterest.
What is Apache Gravitino?
A high‑performance, geographically distributed, federated metadata‑lake management system that provides a unified data & AI asset management platform. It can:
- Unified Metadata Management – Unified models & APIs for diverse data sources.
- Direct Metadata Management – Changes are reflected in real‑time to the underlying systems.
- Multi‑Engine Support – Works with Trino, Spark, Flink, etc.
- Geographically Distributed Deployment – Supports cross‑region, cross‑cloud architectures.
- AI Asset Management – Manages both data assets and AI/ML model metadata.
Core Concepts
| Concept | Description |
|---|---|
| Metalake | Container/tenant for metadata; typically one organization ↔ one metalake. |
| Catalog | Collection of metadata from a specific source. |
| Schema | Second‑level namespace (equivalent to a database schema). |
| Table | Bottom‑level object representing a concrete data table. |
Supported Data Sources
| Category | Types |
|---|---|
| Relational Databases | MySQL, PostgreSQL, OceanBase, Apache Doris, StarRocks, … |
| Big‑Data Storage | Apache Hive, Apache Iceberg, Apache Hudi, Apache Paimon, Delta Lake (in development) |
| Message Queues | Apache Kafka |
| File Systems | HDFS, S3, GCS, Azure Blob Storage, Alibaba Cloud OSS |
| AI/ML Data Formats | Lance (columnar format optimized for AI/ML workloads) |
REST API Services
Gravitino Core REST API
- Full CRUD for all metadata objects (Metalake, Catalog, Schema, Table, …)
- User, group, role, and permission management
- Advanced features: tags, policies, models, etc.
- Authentication: Simple, OAuth2, Kerberos
Iceberg REST Service
- Implements Apache Iceberg REST API spec
- Supports Hive, JDBC, and custom back‑ends as storage
- Table management & query capabilities across S3, HDFS, GCS, Azure, …
Lance REST Service
- Implements Lance REST API spec
- Optimized for AI/ML workloads (vector data storage & retrieval)
- Namespace & table management
Direct Metadata Management
- Real‑time Synchronization – Immediate propagation of metadata changes to underlying sources.
- Bidirectional Synchronization – Sync both from Gravitino → source and source → Gravitino.
- Transaction Support – Guarantees atomicity & consistency of metadata ops.
- Version Management – Metadata version control & historical tracking.
Unified Permission Management
| Feature | Description |
|---|---|
| RBAC | Flexible permission handling for users, groups, and roles. |
| Ownership Model | Every metadata object has a clear owner. |
| Permission Inheritance | Hierarchical inheritance from Metalake down to tables. |
| Fine‑grained Control | Multi‑level permissions (Metalake → Catalog → Schema → Table). |
Supported Permission Types
- User & group management
- Catalog & schema creation
- Read/write on tables, topics, filesets
- Model registration & version control
- Tag & policy application
Data Lineage (OpenLineage)
- Automatic Lineage Collection – Via Spark plugins.
- Unified Identifiers – Normalizes identifiers across sources to Gravitino IDs.
- Multi‑Source Support – Hive, Iceberg, JDBC, file systems, etc.
Deployment Modes
- Single‑node – Development & testing.
- Cluster – High availability & load balancing.
- Kubernetes – Containerized deployment with auto‑scaling.
- Docker – Official Docker images available.
Storage Backends
- Relational DBs: MySQL, PostgreSQL, …
- Distributed storage systems (pluggable).
Authentication Methods
- Simple (username/password)
- OAuth2
- Kerberos (for Hive backends)
Credential Management
- Cloud storage credential vending (S3, GCS, Azure, …)
- Dynamic credential refresh
- Secure credential passing mechanisms
Integration with Compute Engines
Gravitino deeply integrates with mainstream compute engines and data‑processing frameworks, delivering a unified data‑access experience.
- Apache Spark – Seamless metadata synchronization and lineage tracking.
- (Other engines such as Trino, Flink, etc., are also supported.)
Integration Capabilities of Apache Gravitino
Gravitino provides a rich set of connectors and SDKs that let you plug into existing data infrastructures with minimal effort. The following sections outline each integration point and its key features.
🌟 Gravitino Spark Connector
Supports Spark SQL and DataFrame API
- Automatic data‑lineage collection and tracking
- Unified access to multiple data sources
🔎 Trino Connector
Integration through the Gravitino Trino Connector service
- Federated queries across heterogeneous data sources
- High‑performance analytical query capabilities
⚡ Apache Flink Connector
Integration through the Gravitino Flink Connector service
- Stream‑batch unified data processing
- Real‑time data processing and analysis
🐍 PyIceberg
Iceberg table access for Python environments
- Connects to the Gravitino Iceberg REST service
- Enables data‑science and machine‑learning workflows
- Provides Pandas‑compatible data interfaces
🚀 Daft
Modern distributed data‑processing framework
- Optimized for AI/ML workloads
- Supports multimodal data processing
- Integrated with Gravitino metadata management
☸️ Kubernetes
Native deployment on Kubernetes clusters
- Helm charts and Operators for easy installation
- Auto‑scaling and fault‑recovery capabilities
- Integration with cloud‑native monitoring and logging systems
🌐 REST API
Complete RESTful interface for metadata management
- Supports all CRUD operations on catalogs, schemas, tables, and more
- Standardized HTTP endpoints
- Multiple authentication methods (e.g., token, OAuth)
☕ Java SDK
Native Java client library
- Type‑safe API surface
- Built‑in connection pooling and retry mechanisms
- Comprehensive exception handling
🐍 Python SDK
Python client library
- Asynchronous operation support
- Seamless integration with Jupyter notebooks
- Tailored for data‑science workflows
Why These Integrations Matter
These capabilities enable Gravitino to seamlessly integrate into existing data ecosystems, giving users a unified and efficient data‑management experience. Upcoming articles will dive deeper into each component’s configuration and usage patterns—stay tuned!
👉 Continue reading: Setup Guide
⭐️ Follow and star the project: Apache Gravitino Repository
Note: This article reflects the features of Apache Gravitino v1.1.0. For the latest updates, consult the official documentation or open an issue on GitHub.