# Apache Gravitino — Unified Metadata Lake for Data and AI

> Apache Gravitino is a metadata lake that provides a single catalog interface to manage schemas, tables, models, and topics across multiple data sources, query engines, and AI platforms.

## Install

Save in your project root:

# Apache Gravitino — Unified Metadata Lake for Data and AI

## Quick Use
```bash
# Download and start
curl -LO https://github.com/apache/gravitino/releases/latest/download/gravitino.tar.gz
tar xzf gravitino.tar.gz && cd gravitino
./bin/gravitino-server.sh start
# Access the web UI at http://localhost:8090
```

## Introduction
Apache Gravitino is a metadata management platform that unifies catalog operations across heterogeneous data sources and AI systems. Instead of managing separate metadata stores for each engine, Gravitino provides a single entry point for schema, table, model, and topic management.

## What Apache Gravitino Does
- Provides a unified metadata catalog spanning relational databases, data lakes, and messaging systems
- Manages metadata for Hive, Iceberg, JDBC catalogs, Kafka topics, and ML model registries
- Enables cross-engine metadata sharing between Spark, Trino, Flink, and other query engines
- Supports multi-tenant metalakes with role-based access control
- Offers REST, Java, and Python APIs plus a web management UI

## Architecture Overview
Gravitino introduces the concept of a metalake, a top-level namespace that groups catalogs from different data sources. Each catalog connects to a backend system (Hive Metastore, JDBC database, Iceberg REST catalog, Kafka cluster) via provider plugins. The Gravitino server exposes a REST API that translates unified metadata operations into backend-specific calls. An event listener framework enables audit logging and downstream notifications when metadata changes.

## Self-Hosting & Configuration
- Download the release tarball or build from source with Gradle
- Configure gravitino-server.conf with the server port and backend storage settings
- Register catalogs via the REST API or web UI, specifying the provider and connection details
- Set up a relational backend (MySQL or PostgreSQL) for production metadata persistence
- Deploy behind a reverse proxy with TLS for production environments

## Key Features
- Unified catalog interface for Hive, Iceberg, JDBC, Kafka, and model registries
- Metalake concept provides multi-tenant isolation for different teams or projects
- Cross-engine metadata sharing eliminates catalog duplication between Spark, Trino, and Flink
- Tag-based metadata classification and governance across all managed assets
- Event listener framework for audit trails and automated metadata workflows

## Comparison with Similar Tools
- **Hive Metastore** — Hive-centric catalog; Gravitino unifies Hive with Iceberg, JDBC, Kafka, and more
- **Unity Catalog** — Databricks-originated; Gravitino is vendor-neutral and Apache-governed
- **Apache Polaris** — Iceberg-focused catalog; Gravitino covers a broader range of data and AI assets
- **DataHub** — metadata discovery and lineage; Gravitino is an operational catalog for query engines
- **OpenMetadata** — metadata platform; Gravitino serves as an active catalog that engines query directly

## FAQ
**Q: What is a metalake?**
A: A metalake is the top-level organizational unit in Gravitino. It groups multiple catalogs (Hive, Iceberg, JDBC, Kafka) under a single namespace for unified management.

**Q: Which query engines can use Gravitino?**
A: Gravitino provides connectors for Apache Spark, Trino, and Apache Flink. Applications can also use the REST or Java/Python client APIs directly.

**Q: Does Gravitino replace Hive Metastore?**
A: Gravitino can sit in front of Hive Metastore and other catalogs, providing a unified interface. It does not replace the backends but adds a unification layer.

**Q: Is Gravitino production-ready?**
A: Apache Gravitino is an incubating project under the Apache Software Foundation with active development and growing production adoption.

## Sources
- https://github.com/apache/gravitino
- https://gravitino.apache.org/docs/

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Source: https://tokrepo.com/en/workflows/asset-4b259937
Author: AI Open Source