How a Data Catalog Works: Organizing Metadata

A data catalog functions as an index of enterprise data, making it easy for teams to locate, interpret, and use datasets.

Metadata Collection: Organizing Data for Search and Discovery

Metadata is "data about data"—it provides contextual information that allows users to identify, interpret, and manage datasets effectively. A data catalog automatically collects, stores, and organizes three main types of metadata:

For example, a retail company managing customer transaction data would include:

• Technical metadata: Table structure with columns for customer ID, purchase date, and amount.
• Business metadata: Definitions of "customer churn" or "monthly revenue."
• Operational metadata: Last update timestamp and records of user access.

This allows analysts, engineers, and compliance officers to find, trust, and govern data efficiently.

Data Search and Discovery: Finding the Right Data Quickly

A data catalog provides search and discovery capabilities similar to a search engine, allowing users to:

• Search using natural language queries (e.g., "monthly sales report").
• Apply filters such as data source, format, last modified date, or sensitivity classification.
• Browse dataset relationships to understand dependencies and links between different datasets.

For example, a marketing analyst searching for customer retention data can:

1. Look up "customer churn dataset" in the data catalog.
2. View metadata explaining what the dataset contains.
3. Check data lineage to verify how the dataset was generated and its reliability.
4. Request access directly through the catalog without IT intervention.

This reduces manual effort and ensures teams work with trusted, up-to-date data.

Data Lineage: Tracking Where Data Comes From and How It’s Used

Data lineage tracks how data moves, transforms, and integrates across an organization. It helps:

• Trace data errors back to their source.
• Understand dependencies between different datasets.
• Ensure compliance by monitoring how data is used and modified.

For example, a healthcare company analyzing patient claims may use a data catalog to:

• Identify where claims originate (e.g., submitted via a mobile app).
• Track how they are processed (e.g., enriched with policyholder information).
• Verify how the data is used (e.g., for fraud detection or insurance audits).

This ensures transparency and helps organizations maintain data integrity.

Data Governance: Enforcing Access Control and Compliance

Organizations must comply with data privacy and security regulations such as:

• GDPR (General Data Protection Regulation)
• CCPA (California Consumer Privacy Act)
• HIPAA (Health Insurance Portability and Accountability Act)
• SOC 2 (Service Organization Control 2)

A data catalog helps enforce governance by:

• Defining access control policies (e.g., restricting access to sensitive data).
• Classifying and tagging sensitive data (e.g., marking customer PII as confidential).
• Maintaining audit trails (e.g., tracking who accessed or modified data).

For example, in a hospital setting:

• Doctors can access patient medical history but not billing details.
• Compliance teams can monitor data access to ensure privacy policies are followed.

This reduces security risks and ensures that only authorized users can view sensitive data.

Features of a Data Catalog

Core Features

Advanced Features

How Data Catalogs Fit Into the Modern Data Stack

The modern data ecosystem consists of various components such as data lakes, warehouses, analytics engines, and governance frameworks. A data catalog serves as a centralized metadata and governance layer that integrates with these components.

How Data Catalogs Interact with Other Components

A data catalog acts as the metadata backbone for these systems, ensuring seamless interoperability, governance, and access control across the modern data stack.

Popular Data Catalogs: Overview & Comparison

Various data catalogs are available, each designed for different use cases, architectures, and governance requirements. Some focus on technical metadata for query engines (metastores), while others provide business-oriented data discovery and governance features.

Types of Data Catalogs

Comparison of Popular Data Catalogs

Key Takeaways

• Unity Catalog OSS (Open Source) is still evolving, offering governance across multiple table formats but lacks advanced lineage tracking.
• Unity Catalog (Databricks Managed) provides the most comprehensive governance solution for Databricks users.
• Apache Polaris is an Iceberg-native catalog but lacks support for Delta Lake and Hudi.
• AWS Glue is tightly integrated with AWS services but does not enforce access control on raw datasets.
• DataHub and Atlan provide business-friendly discovery & governance but lack direct access control enforcement on raw data.

Conclusion: Why Data Catalogs Matter

A data catalog is not just a tool—it’s a foundational component of data governance and management. As organizations deal with ever-growing data complexity, a well-implemented data catalog ensures:

• Faster data discovery and usability – Employees spend less time searching for the right data.
• Improved data governance and security – Compliance with regulations such as GDPR, CCPA, and HIPAA is streamlined.
• Better collaboration across teams – Business and technical users can access a shared understanding of data.
• Interoperability across data platforms – Seamlessly integrates with data lakes, warehouses, and analytics engines.

With the rise of open table formats (Iceberg, Delta, Hudi) and lakehouse architectures, data catalogs play an increasingly critical role in enabling multi-cloud, AI-driven, and federated analytics strategies.

By implementing a robust data catalog, organizations can unlock the full potential of their data assets, reduce compliance risks, and drive better decision-making across all teams.

FAQs About Data Catalogs

What is the main purpose of a data catalog?

A data catalog serves as a centralized repository that collects, organizes, and manages metadata, making it easier for users to discover, access, and govern data assets. It helps with data searchability, governance, lineage tracking, and access control to ensure compliance and efficiency in data management.

How is a data catalog different from a metastore?

A metastore is a specialized type of data catalog designed to store technical metadata about structured data, typically used for query engines. Examples include the Hive Metastore, AWS Glue, and Unity Catalog (OSS).
A data catalog, on the other hand, extends beyond metadata storage and includes data discovery, governance, lineage tracking, and business metadata to support broader enterprise use cases.

What are the most popular data catalogs?

Some of the most widely used data catalogs include:

• Unity Catalog (OSS & Databricks Managed) – Strong governance & metadata management for open table formats.
• Apache Polaris (Incubating) – Iceberg-native catalog with evolving features.
• AWS Glue – Metadata repository for AWS-based data lakes.
• DataHub – Open-source, business-friendly catalog with AI-driven discovery.
• Atlan – Business-focused catalog with data governance & collaboration tools.

How does a data catalog support open table formats (Iceberg, Delta, Hudi)?

Data catalogs provide metadata management for open table formats, ensuring compatibility across different query engines.

Key Observations:

• Iceberg requires a catalog for metadata storage and transaction management.
• Delta & Hudi have built-in metadata handling but can integrate with external catalogs for broader interoperability.

How do data catalogs enforce access control?

Data catalogs enforce fine-grained access control using RBAC (Role-Based Access Control), ABAC (Attribute-Based Access Control), and IAM integration.

How does StarRocks integrate with data catalogs?

StarRocks, a high-performance analytical database, integrates with data catalogs for metadata management, governance, and access control.

Example Use Case:
A retail company uses StarRocks + Unity Catalog to:

1. Query customer analytics from Iceberg tables stored in AWS S3.
2. Enforce access policies via Unity Catalog IAM integration.
3. Join Delta Lake transaction data with real-time sales insights.

Can data catalogs be used in multi-cloud environments?

Yes, modern data catalogs support multi-cloud governance across AWS, Azure, and GCP.

• Unity Catalog OSS & Polaris work across multiple clouds.
• AWS Glue is AWS-centric and does not extend natively to GCP or Azure.
• DataHub & Atlan provide cross-platform metadata aggregation.

How do data catalogs handle data lineage tracking?

Data catalogs track data movement, transformations, and dependencies across multiple tools.

What are the best data catalogs for AI & Machine Learning governance?

Some catalogs offer AI/ML governance features, such as tracking ML models, feature stores, and experiment metadata.