The modern data landscape is increasingly fragmented. Organizations operate across multiple clouds, hybrid environments, and diverse data processing engines, generating structured, semi-structured, and unstructured data. Managing this data efficiently requires governance, access control, lineage tracking, and discoverability—all of which are the primary functions of a data catalog.
However, not all data catalogs serve the same purpose. Some are technical metastores, designed to store metadata for query engines, while others focus on business metadata, helping analysts and engineers discover and document data assets. With new open table formats like Apache Iceberg, Apache Hudi, and Delta Lake, data catalogs have taken on an even greater role in ensuring seamless interoperability across platforms.
One of the most comprehensive solutions in this space is Unity Catalog, launched by Databricks in 2021. Unlike traditional metastores, Unity Catalog is designed to be a unified and interoperable governance layer that spans both structured and unstructured data assets, including AI models and functions. It streamlines governance by centralizing access control, auditing, lineage tracking, and data discovery across all workspaces. Before its introduction, governance in many organizations was fragmented, with different environments managing access policies and metadata independently, leading to inconsistencies and security challenges.
Recognizing the need for open and standardized governance, Databricks open-sourced Unity Catalog in June 2024, making it the first open-source catalog capable of managing data and AI assets across multiple clouds, formats, and compute engines. This move aimed to break vendor lock-in, enhance interoperability with table formats like Delta, Iceberg, and Hudi, and provide organizations with a governance framework that is both flexible and scalable. By opening Unity Catalog to the broader community, Databricks has positioned it as a foundational component of the modern data ecosystem, ensuring that governance is no longer an obstacle but an enabler of data-driven innovation.
Databricks has a long history of championing open source, contributing to major projects like Apache Spark, MLflow, and Delta Lake. Unity Catalog was initially developed to solve the limitations of the Hive Metastore (HMS), which, despite being widely adopted, lacked several critical capabilities:
Unity Catalog was built to act as a unified governance layer for structured, semi-structured, and unstructured data. Unlike Hive Metastore, which primarily serves as a registry for SQL tables, Unity Catalog extends governance to AI models, user-defined functions (UDFs), and even file-based data assets.
The decision to open-source Unity Catalog was driven by four key goals:
With over 10,000 organizations using Unity Catalog in production, Databricks felt confident that the core architecture and APIs were mature enough to be released as an open-source project.
The decision to open-source Unity Catalog was driven by confidence in its technical maturity. With over 10,000 customers already running Unity Catalog in production, Databricks felt it was the right time to contribute it to the open-source community, enabling wider adoption and further innovation.
Additionally, the Unity Catalog logo itself symbolizes its core functionality: squares representing structured data (tables), triangles symbolizing AI-related assets (models and functions), and hexagons denoting unstructured data (volumes). This visual representation highlights Unity Catalog’s ability to bridge different types of data assets in a seamless manner.
By opening up Unity Catalog, Databricks is not just sharing its governance technology with the community but also advancing the broader vision of an open lakehouse architecture where governance is no longer a bottleneck but a foundational enabler of data-driven innovation.
Before Unity Catalog, companies often faced governance headaches due to several critical issues:
Data governance was often managed separately within each workspace, requiring teams to configure policies independently for different environments. This led to inconsistencies, redundant efforts, and security vulnerabilities. Managing data in silos made it difficult to enforce standardized governance practices across an organization.
User identity and access control were cumbersome to manage across multiple workspaces and platforms. Each system required separate access configurations, leading to administrative overhead and potential misconfigurations. Ensuring consistency in access control policies was a significant challenge.
Understanding how data flows through different transformations and pipelines was difficult. Without clear lineage tracking, teams struggled to trace the origin and transformations of datasets, making debugging, compliance, and performance optimization more complicated. Maintaining data quality and ensuring consistency across various teams was another persistent issue.
Regulations such as GDPR, CCPA, and industry-specific standards require stringent data governance, access control, and auditability. However, enforcing these requirements in a distributed data environment with fragmented governance structures was difficult, increasing the risk of non-compliance and data breaches.
As organizations expanded across cloud providers such as AWS, Azure, and GCP, ensuring a consistent governance framework became increasingly complex. Different cloud environments introduced variations in how data was accessed and managed, making it difficult to implement a unified governance strategy at scale.
The metastore serves as the top-level container for metadata management and access control. Unlike traditional Hive Metastore (HMS), which operates at a per-cluster or per-workspace level, Unity Catalog’s metastore is globally accessible across all Databricks workspaces in an organization.
Key Advantages of the Unity Metastore:
A catalog in Unity Catalog is a logical collection of schemas (databases). It organizes and isolates data assets based on business domains, teams, or environments (such as development, staging, and production).
Key Benefits of Catalogs:
A schema, also referred to as a database, is a logical collection of tables and views within a catalog. Schemas further help structure and categorize data assets within an organization.
Key Features of Schemas:
Tables in Unity Catalog are structured collections of data stored in a table format such as Delta Lake, Iceberg, or Hudi. Unity Catalog also supports views, which are virtual tables derived from one or more tables through SQL queries.
Key Enhancements Over Traditional Data Management Approaches:
Identity Federation in Unity Catalog simplifies authentication and access management across all workspaces and cloud providers. Rather than managing access at the workspace level, Unity Catalog federates identities at the account level using industry standards such as OAuth, SCIM, and IAM policies.
Benefits of Identity Federation:
Unity Catalog enables fine-grained, role-based access control (RBAC) through Access Control Lists (ACLs).
How ACLs Work in Unity Catalog:
Unlike legacy metastores, Unity Catalog’s ACLs extend beyond tabular data to include AI models, user-defined functions (UDFs), and unstructured data assets.
One of the most powerful capabilities in Unity Catalog is automated lineage tracking and data quality monitoring.
Data Lineage in Unity Catalog:
Data Quality Monitoring:
Choosing the right catalog depends on your use case, architectural constraints, and governance needs. To understand where Unity Catalog fits, let's compare it to Apache Polaris, DataHub, AWS Glue, and other catalog offerings.
| Feature | Unity Catalog OSS | Unity Catalog (Databricks) | Apache Polaris | AWS Glue | DataHub | Atlan |
|---|---|---|---|---|---|---|
| Open Table Format Support | Delta, Iceberg, Hudi | Delta, Iceberg (Uniform for Hudi) | Iceberg Only | Delta, Iceberg, Hudi | Delta, Iceberg, Hudi | Delta, Iceberg |
| Unstructured Data Governance | ✅ Volumes for files | ✅ Volumes for files | ❌ No | ❌ No | ❌ No | ❌ No |
| Identity Federation | ✅ OAuth & IAM | ✅ SCIM, OAuth, IAM | ⚠️ OAuth Client Secrets | ✅ AWS IAM | ✅ OAuth | ✅ OAuth |
| Query Engine Compatibility | Spark, DuckDB, Daft | Spark, DuckDB, Trino | Iceberg-compatible only | AWS services (Athena, Redshift) | Multiple | Multiple |
| Data Lineage | ❌ No | ✅ Spark, SQL lineage | ❌ No | ❌ No | ✅ Rich lineage | ✅ Rich lineage |
| Data Discovery & Search | 🔍 Basic schema browsing | 🔍 AI-enhanced search | 🔍 Basic metadata search | 🔍 Basic metadata search | 🔍 Advanced search, filtering | 🔍 Advanced search, filtering |
| Access Control | ✅ Storage-based auth | ✅ Storage-based auth | ✅ RBAC | ❌ Metadata only | ❌ Metadata only | ❌ Metadata only |
As organizations scale their data infrastructure, they often face challenges in balancing performance, interoperability, and governance. While modern query engines provide fast analytics, ensuring consistent access control, metadata management, and interoperability across diverse storage formats remains a challenge. StarRocks and Unity Catalog address these concerns by integrating high-performance SQL querying with centralized governance and metadata management.
Unity Catalog provides a centralized metadata and governance layer for managing structured, semi-structured, and unstructured data. It enforces fine-grained access control, maintains data lineage, and supports multiple table formats such as Delta Lake, Apache Iceberg, and Apache Hudi.
StarRocks, an open-source analytical database, is optimized for low-latency, high-concurrency queries on large datasets. It integrates with Delta Lake via Delta Kernel Java, allowing it to efficiently query Delta tables managed within Unity Catalog.
This combination enables:
Interoperability Across Table Formats
Centralized Security and Access Control
High-Performance Query Execution
Cross-Engine Federated Analytics
Databricks Unity Catalog is a powerful open-source metadata and governance solution that streamlines data access, security, and lineage tracking. By supporting open table formats and multi-cloud environments, it provides a scalable governance layer for modern data lakes and lakehouses.
With interoperability across query engines like StarRocks, Trino, and Spark, organizations can unlock new levels of performance, real-time analytics, and AI-driven workloads while maintaining a strong governance posture.
StarRocks, as a high-performance analytical engine, extends Unity Catalog’s capabilities, making it easier to query governed data at scale. Whether for BI, federated analytics, or real-time AI, StarRocks and Unity Catalog together offer an efficient and scalable solution for the modern data stack.
Unity Catalog provides fine-grained access control through role-based access control (RBAC) and attribute-based access control (ABAC). It supports:
This ensures consistent security enforcement across compute engines such as Spark, Trino, DuckDB, and StarRocks.
Yes, Unity Catalog governs structured, semi-structured, and unstructured data. It extends beyond traditional table metadata to manage:
StarRocks, a high-performance analytical database, integrates with Unity Catalog by:
This integration enables fast, real-time analytics on governed data stored across multiple table formats.
Yes, Unity Catalog supports real-time data management through Delta Lake and Apache Hudi:
This makes Unity Catalog well-suited for real-time analytics, data pipelines, and AI applications.
Yes, Unity Catalog is cloud-agnostic and supports:
This ensures consistent metadata management, access control, and lineage tracking across different cloud providers.
Unity Catalog integrates with:
Because it supports open table formats, Unity Catalog can work with any engine that supports Delta, Iceberg, or Hudi.
Unity Catalog automatically tracks lineage for data transformations, providing:
This ensures transparency in data transformations for debugging, compliance, and governance.
Yes, Unity Catalog is a modern alternative to Hive Metastore (HMS), offering:
Organizations migrating from HMS to Unity Catalog benefit from stronger interoperability, access control, and scalability.
Unity Catalog supports:
This flexibility allows organizations to use the best table format for their specific needs while maintaining governance.
Unity Catalog provides:
This makes it easier to enforce security, manage access, and track data usage across an enterprise.
Yes, the open-source version of Unity Catalog can be used outside of Databricks, providing:
However, some advanced features like AI-driven discovery and managed access control are exclusive to the Databricks-managed version.
Unity Catalog extends data governance to AI models and ML artifacts, including:
This integration helps organizations maintain governance over AI and ML workloads in regulated environments.
Unity Catalog supports:
This ensures data consistency while allowing for controlled schema modifications.