Understanding Data Catalogs: Features, Comparisons, and Use Cases

Written by Admin | Feb 5, 2024 8:01:23 PM

What is a Data Catalog?

Organizations today store vast amounts of data across multiple platforms, including databases, cloud storage, data lakes, and business applications. However, as data grows, it becomes increasingly difficult to track where it resides, understand its context, determine ownership, and ensure its proper usage.

For example, a large financial institution may have:

Customer transaction data stored in a data warehouse such as Snowflake or BigQuery.
Marketing campaign performance data in Google Analytics or Adobe Analytics.
Product sales and supply chain data in an Enterprise Resource Planning (ERP) system.

Without a centralized system for organizing and documenting data, different teams may face challenges such as:

Data discovery issues – Difficulty in locating the right dataset for analysis.
Inconsistent definitions – Teams using different interpretations of key business metrics (e.g., "customer churn" or "monthly revenue").
Redundant effort – Duplication of reports, inconsistent ETL processes, and wasted time reconciling data discrepancies.

A data catalog addresses these challenges by acting as a searchable inventory of an organization's data assets. It enables users to discover, understand, and govern data efficiently.

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:

Type of Metadata	Description	Example
Technical Metadata	Describes how data is stored, formatted, and processed.	Table name, column types, file format, schema definitions.
Business Metadata	Adds business context to data, making it easier for non-technical users to understand.	Data descriptions, KPIs, business terms, tags.
Operational Metadata	Tracks how data is accessed, modified, and used over time.	Data lineage, refresh frequency, access logs, user annotations.

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:

Look up "customer churn dataset" in the data catalog.
View metadata explaining what the dataset contains.
Check data lineage to verify how the dataset was generated and its reliability.
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

Feature	Description	Purpose
Metadata Management	Collects and organizes technical, business, and operational metadata.	Ensures data is searchable and well-documented.
Data Discovery & Search	Provides keyword-based search, filtering, and categorization.	Helps users find the right datasets quickly.
Data Lineage Tracking	Maps data movement and transformations across systems.	Improves transparency and compliance.
Access Control & Security	Supports Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC).	Restricts access to sensitive data.
Business Glossary & Tags	Defines business terms and dataset relationships.	Ensures consistent terminology and understanding.
Integration with ETL & BI Tools	Connects with analytics, ETL, and cloud storage platforms.	Facilitates seamless data integration.

Advanced Features

Feature	Description	Benefit
Automated Metadata Ingestion	Uses AI/ML to extract metadata from various sources.	Reduces manual work and improves data accuracy.
AI-Powered Recommendations	Suggests relevant datasets based on user behavior.	Enhances data usability and efficiency.
Automated Data Classification	Identifies and tags sensitive data (e.g., PII).	Strengthens security and compliance.
Federated Search	Searches across multiple platforms (SQL, NoSQL, data lakes, cloud storage).	Provides unified data access across hybrid environments.

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

Component	Role	How a Data Catalog Integrates
Data Lake	Stores raw, semi-structured, and structured data.	Organizes metadata, schemas, and access control for data stored in S3, ADLS, GCS.
Data Warehouse	Stores structured, analytics-ready data.	Tracks datasets stored in Snowflake, BigQuery, Redshift for structured analytics.
Query Engines	Analyzes data from multiple sources.	Connects metadata to Trino, StarRocks, Spark for optimized queries.
BI Tools	Provides dashboards and reports.	Supplies dataset definitions, lineage, and data quality indicators for BI tools like Looker, Tableau, Power BI.
Data Governance	Ensures security, compliance, and auditing.	Enforces access control, policy-based security, and regulatory compliance.

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

Catalog Type	Example Catalogs	Primary Function	Strengths	Weaknesses
Metastore Catalogs	Apache Hive Metastore, AWS Glue, Apache Polaris (Incubating), Unity Catalog (OSS)	Stores metadata for query engines & open table formats	Supports Iceberg, Delta, Hudi	Limited governance features
Business-Focused Data Catalogs	Atlan, DataHub, Collibra, Alation	Business metadata management, search & discovery, documentation	Rich UI, collaboration tools, business glossary	Does not enforce access control on raw data
Hybrid Metadata & Governance Catalogs	Databricks Unity Catalog (Managed), Gravitino	Centralized metadata + governance	Metadata storage + access control + lineage tracking	Limited external connectors
Catalog Aggregators	DataHub, OpenMetadata, Acryl	Unifies multiple catalogs & sources	Aggregates metadata across platforms	Cannot enforce security on raw data

Comparison of Popular Data Catalogs

Feature	Unity Catalog (OSS)	Apache Polaris	AWS Glue	DataHub	Atlan
Open Source	✅ Yes (since 2024)	✅ Yes (Apache Incubating)	❌ No	✅ Yes	❌ No
Governance Features	✅ Access Control, Identity Federation	⚠️ Basic IAM & OAuth Controls	❌ No direct enforcement	✅ Role-Based Access Control (RBAC)	✅ RBAC & Business Metadata
Table Format Support	✅ Delta, Iceberg, Hudi	✅ Iceberg Only	✅ Delta, Iceberg, Hudi	✅ Delta, Iceberg, Hudi	✅ Delta, Iceberg
Query Engine Compatibility	✅ Spark, Trino, DuckDB	✅ Iceberg-compatible engines	✅ AWS Athena, Redshift	✅ Multi-engine (Spark, Presto, Trino, Snowflake)	✅ Multi-engine
Data Discovery & Search	🔍 Basic schema browsing	🔍 Basic search	🔍 Limited search	🔍 Advanced search & filtering	🔍 Advanced search & personalization
Data Lineage	❌ No	❌ No	❌ No	✅ Rich column-level lineage	✅ Rich column-level lineage
Unstructured Data Governance	✅ Volumes for unstructured data	❌ No	❌ No	❌ No	❌ No

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.

Feature	Metastore (e.g., Hive, Glue, Polaris, Unity OSS)	Data Catalog (e.g., Atlan, DataHub, Unity Managed)
Metadata Storage	✅ Stores table schemas & locations	✅ Stores schemas, business metadata, lineage
Data Discovery & Search	❌ Limited	✅ Advanced search & filters
Access Control	✅ Basic access management	✅ Role-Based (RBAC), Attribute-Based (ABAC)
Lineage Tracking	❌ No or limited	✅ Full data lineage
Compliance & Security	❌ No governance tools	✅ Compliance tagging, policy enforcement

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.

Table Format	Requires a Catalog?	Compatible Data Catalogs
Apache Iceberg	✅ Yes (Catalog-dependent)	Apache Polaris, Unity, Glue, DataHub
Delta Lake	❌ No (Native metadata management)	Unity, Glue, DataHub
Apache Hudi	❌ No (Optional Catalog Use)	Unity, Glue, DataHub

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.

Catalog	Supports RBAC?	IAM Integration	Column-Level Security
Unity Catalog (OSS)	✅ Yes	✅ Supports Google, Okta, SCIM	✅ Column ACLs possible
Unity Catalog (Databricks Managed)	✅ Yes	✅ SCIM, IAM	✅ Fine-grained control
Apache Polaris	✅ Yes	⚠️ Basic IAM	❌ No
DataHub	✅ Yes	✅ Identity provider support	✅ Column security

How does StarRocks integrate with data catalogs?

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

Feature	StarRocks Integration
Query Open Table Formats	✅ Supports Iceberg, Delta, Hudi via Delta Kernel Java
Metadata Management	✅ Reads metadata from Unity Catalog, AWS Glue, Polaris
Federated Queries	✅ Joins data from lakes, warehouses, and object storage
Access Control	✅ Uses RBAC from Unity Catalog for security enforcement

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

Query customer analytics from Iceberg tables stored in AWS S3.
Enforce access policies via Unity Catalog IAM integration.
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.

Feature	Unity Catalog OSS	Unity Catalog (Databricks Managed)	DataHub	Atlan
Table-Level Lineage	❌ No	✅ Yes (Spark & SQL)	✅ Yes	✅ Yes
Column-Level Lineage	❌ No	✅ Yes	✅ Yes	✅ Yes
Cross-Engine Lineage	❌ No	✅ Spark, SQL	✅ Multi-engine	✅ Multi-engine

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.

Feature	Unity Catalog (Databricks Managed)	DataHub	Atlan
ML Model Governance	✅ Manages AI models & UDFs	✅ Tracks ML assets	✅ Tracks ML assets
Feature Store Integration	✅ Delta Sharing for ML	✅ Yes	✅ Yes
Data Lineage for AI Workflows	✅ Spark & SQL lineage	✅ Yes	✅ Yes

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