Stop and consider your data architecture right now. You are likely grappling with these challenges:
1. Are you facing vendor lock-in and prohibitive costs?
2. Is fragmented metadata wasting your engineering resources?
3. Are your BI systems failing your AI strategy?
These three questions directly point to the core friction points stemming from metadata constraints, which are crippling modern data teams:
Vendor Lock-in Risk:
Platform-bound catalogs, particularly Databricks Unity Catalog (UC), tie governance and security tightly to a specific computing environment. This limits the freedom to choose best-of-breed engines (Trino, Flink, Ray, and others) and results in prohibitive migration costs and annually increasing spend.
Fragmentation & Operational Complexity:
Modern teams operate in multi-cloud, multi-engine complexity. They lack a unified interface to manage metadata across different clouds (S3, Azure Blob, on-premises, etc) and varying processing engines, severely hindering operational efficiency.
Multimodal Data Silos:
Traditional, table-centric metadata systems fail to natively support AI/LLM workloads, leading to silos for critical Vector Embeddings, Streaming Topics (Kafka), and Model Registries. The lack of unified metadata breaks end-to-end AI lineage and reproducibility.
The reality is, the modern data stack is facing a “fragmentation crisis.” Your metadata, which should be the bridge for unified governance, has instead become the primary casualty of this fragmentation.
We acknowledge that platform-specific solutions like Databricks Unity Catalog (UC) deliver a smooth experience within their own ecosystem. However, for organizations operating across multiple clouds, engines, and formats, that tight integration quickly transforms into a constraint rather than a strength. Traditional systems (HMS, AWS Glue) struggle to keep up, while tightly coupled solutions like UC introduce “soft lock-in.” Here, the core metadata, the true ownership layer of the data, becomes inseparable from a single commercial platform. Once metadata is trapped, everything upstream and downstream hardens around that dependency.
This challenge is amplified dramatically in the AI-Native Era. LLM and agent-based workflows absolutely depend on the ability to discover, understand, trace, and govern all data assets. Without unified metadata, AI pipelines lose transparency, governance becomes brittle, and trust in AI outcomes erodes.
Therefore, the truly essential requirement is a vendor-neutral metadata layer that can unify your entire data ecosystem.
This layer must abstract metadata from compute, storage, and individual vendors; provide consistent semantics across all engines; and support the multimodal assets required for AI.
In short, you need Metadata Freedom. This freedom is the foundation for long-term data agility and AI readiness. It ensures the true “deed” to your data remains in your hands, not locked behind the boundaries of any single cloud, engine, or commercial platform.
The Architecture of Freedom: Why Gravitino Breaks the Mold
Databricks has become one of the most influential players in the modern data stack, and Unity Catalog brings strong cohesion within the Databricks ecosystem.
However, no enterprise is 100% Databricks-only. Most operate across Snowflake, BigQuery, Redshift, Trino, Iceberg, MySQL, S3, and more. Even a powerful platform-native catalog cannot serve as the unified source of metadata truth for such a heterogeneous world.
To bridge this gap and finally achieve cross-platform metadata freedom, we introduce Apache™ Gravitino.
“Metadata freedom is not a feature; it is the foundation on which future-proof data architectures are built.”
— JP Du, PMC member of Apache Gravitino™ project, CEO & Co-founder of Datastrato.
Apache Gravitino™ is not merely a replacement for existing catalogs; it is a foundational architectural shift designed to achieve true Metadata Freedom. We realize this vision by adhering to two core principles: Radical Decoupling and Federated Unification.
Radical Decoupling: Metadata as an Independent Layer
Rather than inheriting constraints from compute or storage platforms, Gravitino treats metadata as an independent, universal control layer.
Compute-agnostic: Works seamlessly with Trino, Spark, Flink, PyTorch, Ray, and others — without imposing a preferred engine.
Storage-agnostic: A connector-based architecture supports S3, GCS, Azure Blob, HDFS, and on-prem object stores.
Vendor-neutral: As described earlier, Gravitino’s decoupled design breaks metadata free from compute, storage, and vendor boundaries, aligning with open standards instead of proprietary roadmaps.
Federated Unification: The Catalog of Catalogs
To solve the multi-engine, multi-cloud “fragmentation crisis” introduced earlier, Gravitino redefines metadata management through its “Federated Metadata Lake” positioning. This architecture unifies the ecosystem without compromising the autonomy of underlying systems.
Gravitino addresses fragmentation with a federated architecture that unifies metadata across engines and clouds without forcing standardization.
AI-native multimodal metadata: Supports tables, unstructured files, Kafka topics, vector embeddings, and models as first-class assets.
Federated control: Gravitino’s “Catalog of Catalogs” integrates with HMS, Apache Apache™Iceberg REST Catalog, MySQL, PostgreSQL, and object stores — unifying governance without replacing existing systems.
This model harmonizes metadata across legacy systems, cloud warehouses, lakehouses, and AI platforms.
Apache Gravitino™ vs. Unity Catalog: Solving the Fragmentation Between All Catalogs
Gravitino is not simply a “better Unity Catalog.” It solves the fragmentation between all catalogs. While UC excels within its platform boundaries, Gravitino is designed to function as the superset control plane for the entire heterogeneous stack.
Community Over Code: Building the Future Through Open Collaboration
For us, the creators and initial contributors to Apache Gravitino™, Metadata Freedom is more than just code. We believe the future of enterprise data architectures cannot be tied to a single corporate roadmap; it must be built by the people who use it. This focus on developer interaction and shared ownership is why we chose the Apache path.
Apache Gravitino™ is governed by the Apache Software Foundation (ASF). This isn’t a mere badge; it’s a structural guarantee of neutrality. We cherish the open, democratic nature of the ASF model, where every major decision, feature, and release is subject to a community-wide consensus and voting process. This rigorous governance ensures that Gravitino’s evolution is driven purely by technical merit and user needs, making it a project that truly belongs to everyone.
In addition to advancing the Apache Gravitino™ project itself, we actively contribute to the broader open-source ecosystem, submitting code to upstream and downstream projects such as Apache Iceberg™, Lance, Daft, OpenLineage, Spark and others.
This commitment to open, multi-company governance directly fuels Gravitino’s rapid momentum. Compared to proprietary-led open source solutions like OSS Unity Catalog, our community activity metrics including lines of code, contributors, commits, and issues have been observed at over 5x in recent periods. This explosion of activity proves that our neutral, democratic approach is exactly what the industry demands, assuring all contributors and adopters of the project’s long-term health and vitality.
Fully embracing the Apache spirit of “Community over code”, we have established the “Data for AI” community. This focused hub convenes developers globally to exchange knowledge and collectively tackle the practical industry challenges unique to modern data infrastructure. To accelerate this collective understanding, we regularly host technical events, inviting leading data experts from cutting-edge companies such as AWS, OpenAI, NVIDIA, Uber, Pinterest, and Roku to share their latest best practices and trends. By fostering this interaction and insight sharing, we ensure Gravitino evolves in lockstep with the most urgent needs of the industry.
Databricks Is Part of the Future — But Not the Whole Future.
This is not a Databricks critique.
They are a key pillar of the ecosystem, and many workloads fit beautifully within their platform. But the modern enterprise will always be a polyglot environment.
We firmly believe that enterprises welcome more flexible, open, and diverse technological solutions. No organization wants to be forced into a corner, locked in by a single supplier.
If your data strategy is not 100% committed to Databricks, or any other singular vendor ecosystem, or if you are implementing a multi-cloud, multi-engine strategy, relying on platform-specific catalogs is fundamentally insufficient.
The path forward requires decoupling control from computing.
Metadata Freedom provides the agility, interoperability, and ultimate safeguard against vendor lock-in.
AI-Native and multimodal workloads demand an open, federated metadata layer to unify tables, files, streams, and vectors.
Apache Gravitino™ demonstrates what the next-generation metadata architecture looks like. Through radical decoupling and a community-driven, federated approach, it returns the sovereignty of metadata to the user.
The future belongs to open, federated, and community-driven metadata.
This is Metadata Freedom. This is what Apache Gravitino™ stands for.
Why wait for tomorrow? Join the Apache Gravitino™ community today and start your journey to Metadata Freedom now.
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