Top 35 Data Catalog Tools in 2025: Features, Use Cases & Buyer Guide

With an abundance of data catalog tools available in 2025, selecting the right one for your organization's specific needs can be daunting. This comprehensive guide evaluates the top 35 data catalog tools based on practical use cases, organizational maturity, and team roles.

In 2025, the data catalog landscape is saturated with tools that, on the surface, appear similar. However, beneath the surface, these tools differ significantly in functionality, integration capabilities, and user experience. The challenge lies in identifying a tool that aligns with your organization's data maturity, governance requirements, and user personas.

Traditional evaluation methods, which focus primarily on feature checklists, often fall short. Instead, organizations need to assess tools based on how well they support the stages of data adoption: Crawl (connect & inventory), Curate (classify & govern), and Consume (search & use), and how they cater to different user roles within the organization.

We’ve segmented the tools into three main categories:

Legacy Enterprise 

Legacy enterprise data catalog tools have established themselves over years as comprehensive solutions for large organizations with complex data environments. These platforms typically offer robust governance, extensive integrations, and scalability tailored to enterprises with mature data management needs.

Tools under consideration:

• Informatica Enterprise Data Catalog

• Collibra Data Intelligence Platform

• IBM Watson Knowledge Catalog

• SAP Data Intelligence

• Oracle Enterprise Metadata Management

• Microsoft Purview

• Alteryx Connect

• Talend Data Catalog

• Amazon Glue Data Catalog

• Google Cloud Data Catalog

• BigID

• Unifi Data Catalog

1. Informatica Enterprise Data Catalog

Informatica Enterprise Data Catalog is renowned for its robust metadata management and data discovery capabilities.

Key Strengths:

• Comprehensive metadata scanning and automated classification.

• Advanced lineage and impact analysis features.

• AI-driven semantic search and recommendations.

Ideal For: Large enterprises with complex data environments seeking robust metadata automation and governance.

Limitations: Users have noted that while the product is reliable and continually improving, it can be pricey and may require significant investment in the Informatica ecosystem.

2. Collibra Data Intelligence Platform

Collibra offers a comprehensive data intelligence platform focused on data governance and stewardship.

Key Strengths:

• Rich governance workflows with stewardship and policy management.

• Business glossary and data dictionary for collaborative curation.

• Integration with various BI, ETL, and cloud tools.

Ideal For: Enterprises prioritizing compliance, stewardship, and regulatory alignment.

Limitations: Some users mention that while the interface is user-friendly, the platform's complexity can lead to a steep learning curve.

3. IBM Watson Knowledge Catalog

IBM’s Watson Knowledge Catalog supports cloud-native governance and AI-readiness.

Key Strengths:

• Embedded data quality scoring and profiling.

• Integration with IBM Cloud Pak for Data.

• Support for AI model governance.

Ideal For: Enterprises using IBM Cloud or investing in AI governance.

Limitations: While the tool is user-friendly and offers fast data access, some users have reported challenges with its design and usability.

4. SAP Data Intelligence

SAP Data Intelligence provides metadata and pipeline orchestration tightly coupled with the SAP stack.

Key Strengths:

• Metadata discovery across SAP and non-SAP landscapes.

• Visual pipeline modeling.

• Machine learning operations support.

Ideal For: Enterprises heavily invested in SAP ecosystems.

Limitations: Users have noted difficulties in integrating SAP Data Intelligence with non-SAP systems like PostgreSQL.

5. Oracle Enterprise Metadata Management

Oracle’s catalog offers lineage and impact analysis across Oracle tools and databases.

Key Strengths:

• End-to-end lineage across Oracle products.

• Deep integration with Oracle Database and ODI.

• Metadata versioning and change tracking.

Ideal For: Oracle-heavy environments focused on lineage and governance.

Limitations: Some users mention a learning curve and challenges with integrating non-Oracle tools.

6. Microsoft Purview

Purview is Microsoft’s unified data governance solution in Azure.

Key Strengths:

• Native integration with Azure services and Microsoft 365.

• Automated classification and data mapping.

• Role-based access controls and policy enforcement.

Ideal For: Organizations embedded in the Microsoft Azure ecosystem.

Limitations: While users appreciate its streamlined data governance capabilities, some have reported challenges with its user interface and integration outside the Azure ecosystem.

7. Alteryx Connect

Alteryx Connect offers metadata discovery tightly integrated with Alteryx workflows.

Key Strengths:

• Searchable data assets with lineage tracking.
• Built-in business glossary and collaboration features.
• Integration with Alteryx Designer for data preparation.

Ideal For: Organizations using Alteryx for self-service analytics.

Limitations: Users have reported that the Designer GUI can be slow and may crash during extended use.

8. Talend Data Catalog

Talend’s catalog supports metadata and governance alongside its integration tools.

Key Strengths:

• Automated data discovery and classification.

• Data lineage and version tracking.

• Strong integration with Talend pipelines.

Ideal For: Enterprises using Talend’s broader data integration suite.

Limitations: Some users have expressed dissatisfaction with Talend, citing it as not providing valuable skills for data engineers and being less favorable compared to other tools.

9. Amazon Glue Data Catalog

Glue Data Catalog is AWS’s metadata store for data lakes and analytics.

Key Strengths:

• Serverless metadata catalog with integration across AWS services.

• Support for Apache Hive and Spark.

• Central metadata repository for Athena, Redshift, and EMR.

Ideal For: Teams operating primarily within the AWS ecosystem.

Limitations: Users have noted that while AWS Glue simplifies ETL processes, it has a learning curve and may not be as polished as other products.

10. Google Cloud Data Catalog

Google Cloud’s metadata service focuses on search and discovery.

Key Strengths:

• Tag-based metadata classification.

• Integrated with BigQuery and Looker.

• Support for data governance via policy tags.

Ideal For: Cloud-first teams using GCP-native services.

Limitations: Users have mentioned that while the search functionality is powerful, the platform may lack advanced curation workflows.

11. BigID

BigID is centered around privacy, security, and data risk governance.

Key Strengths:

• Sensitive data discovery and classification.

• Identity-aware governance and compliance tooling.

• Risk scoring and policy automation.

Ideal For: Enterprises prioritizing data privacy, security, and compliance.

Limitations: Users have reported that BigID can be expensive and may experience latency issues, especially when integrating with legacy systems.

12. Unifi Data Catalog

Unifi offers AI-driven cataloging and governance for enterprise data estates.

Key Strengths:

• Automated cataloging with ML-based recommendations.

• Self-service data discovery.

• Integration with enterprise data lakes and warehouses.

Ideal For: Organizations seeking intelligent metadata enrichment.

Limitations: While users find Unifi easy to implement and use, some have noted that its platform evolution and roadmap clarity may vary. 

Modern data catalogs

Modern data catalog tools balance powerful features with ease of use and agility, catering to growing organizations that require strong metadata management without the overhead of legacy enterprise systems. These platforms often emphasize collaboration, automation, and cloud-native capabilities.

Tools under consideration:

• OvalEdge

• Atlan

• Data.World

• Secoda

• Select Star

• Castor

• Metaphor

• Zeenea

• Stemma

• Crux

• Alex Solutions

• InfoZoom

• Satori

• Anomalo

• SecuPi

13. OvalEdge

OvalEdge offers a unified data catalog platform with a practical blend of deep connectivity, intelligent automation, and contextual collaboration, making it ideal for organizations scaling their data governance efforts without sacrificing flexibility.

Key capabilities:

• Connect broadly, catalog deeply: 150+ native connectors capture both active (e.g., usage, access logs) and extended (e.g., descriptions, policies) metadata.

• Automate smarter: Built-in automation for lineage generation, PII detection, and business rule classification accelerates curation.

• Prioritize & delegate: Popularity and importance scores help teams focus efforts; steward recommendations streamline ownership.

• Actionable interface: Users can request access, explore metrics, and enforce policies directly from the catalog.

• AskEdge+ (AI assistant): Lets users ask analytical questions in natural language and get contextual insights instantly.

Best for: Mid-to-large enterprises seeking a scalable platform to unify fragmented metadata and operationalize governance with strong role-based adoption.

Considerations:

• Designed to be both wide (across systems) and deep (within assets), which makes it ideal for federated environments.

• Strong automation makes it easier to onboard, but governance success still depends on user engagement and process maturity.

14. Atlan

Atlan is a modern data catalog that activates metadata, enabling powerful programmatic use cases through automation and simplifying adoption.

Key Strengths:

• Intuitive user interface with robust product features.
• Compatibility with modern data stacks like Snowflake, dbt, and Sigma Computing.
• Integration with enterprise tools such as Slack and Google Workspace.

Ideal For: Organizations looking for a user-friendly data catalog that integrates seamlessly with modern data tools.

Limitations: Some users have reported challenges with the platform's complexity leading to a steep learning curve.

15. Data.world

data.world is a data catalog and governance platform built on a unique knowledge graph foundation.

Key Strengths:

• Ease of use and intuitive interface.

• Enhances data governance models and improves user experience.

• Enables developers to gain actionable insights.

Ideal For: Organizations seeking a user-friendly platform to enhance data governance and collaboration.

Limitations: Users have noted that while the platform is user-friendly, it may lack some advanced features found in other tools.

16. Secoda

Secoda is a data catalog that centralizes data sources, enhances knowledge documentation, and supports robust governance and quality assurance processes.

Key Strengths:

• Ease of use and great autonomy in configuration.

• Facilitates collaboration across users.

• Links together relevant data sets for projects.

Ideal For: Organizations looking for a straightforward data catalog that enhances collaboration and documentation.

Limitations: Some users have reported that while the tool is easy to use, it may not offer the full range of functionalities found in more comprehensive platforms.

17. Select Star

Select Star is a data catalog known for its detailed column/field-level lineage feature that enables precise tracking of data origins and transformations.

Key Strengths:

• Highly detailed lineage tracking.

• Cost-effective solution for small-to-medium size companies.

• User-friendly interface.

Ideal For: Small to medium-sized companies seeking detailed data lineage capabilities.

Limitations: Users have noted that while the tool is cost-effective, it may lack some advanced features found in more comprehensive platforms.

18. CastorDoc

CastorDoc is an automated data catalog tool that improves data documentation and organizes it for easy access.

Key Strengths:

• AI-generated descriptions for data assets.

• Comprehensive information about data objects.

• Strong security features and responsive customer support.

Ideal For: Organizations looking for an intuitive and efficient data governance tool.

Limitations: Some users have reported that to get the most out of Castor, it's best to use a tech stack it is ready to integrate with.

19. Metaphor by KPMG

Metaphor streamlines data management by blending robust governance and lineage with user-friendly features.

Key Strengths:

• Ensures data integrity and traceability.

• Boosts collaboration across departments.

• Efficient search and discovery tools.

Ideal For: Organizations seeking a data catalog that enhances collaboration and data understanding.

Limitations: Users have noted that while Metaphor offers robust features, it may lack some advanced functionalities found in other tools. 

20. Zeenea

Zeenea offers a data discovery platform with seamless connectivity to various data sources through its extensive collection of native data source connectors.

Key Strengths:

• Extensive native data source connectors.
• Facilitates data discovery across various platforms.

Ideal For: Organizations looking for a data catalog with broad connectivity options.

Limitations: Some users have reported that the interface is outdated and difficult to use, with limited functionality.

21. Stemma by Teradata

Stemma provides enterprise capabilities such as richer automated metadata through intelligence based on common usage patterns.

Key Strengths:

• Automated metadata generation.

• Simplifies adoption with user-friendly features.

Ideal For: Organizations seeking a modern data catalog with automated metadata capabilities.

Limitations: Users have noted that while Stemma offers robust features, it may lack some advanced functionalities found in other tools.

22. Crux

Cruzx is a GenAI-powered decision intelligence platform that enables users to interact with enterprise data through a conversational experience, similar to ChatGPT.

Key Strengths:

• High user satisfaction.

• Comprehensive analytics capabilities.

Ideal For: Organizations seeking a business intelligence platform with strong analytics features.

Limitations: Users have noted that while Crux offers robust analytics capabilities, it may lack some advanced data catalog features.

23. Alex Solutions

Alex Solutions is a metadata management tool that helps manage all work-related data effectively.

Key Strengths:

• Comprehensive data management capabilities.

• User-friendly interface.

Ideal For: Organizations seeking a robust metadata management tool.

Limitations: Some users have reported that while Alex Solutions offers comprehensive features, it may lack some advanced functionalities found in other tools.

24. InfoZoom

InfoZoom is an intuitive data analysis tool that converts large datasets into simple visuals, making it ideal for creating rapid reports in sectors like auditing.

Key Strengths:

• Ease of use and simplicity.

• Rapid report generation capabilities.

Ideal For: Smaller businesses or teams with limited technical expertise needing straightforward data analysis and reporting solutions.

Limitations: Users have noted that while InfoZoom is user-friendly, it may lack advanced features and customization options.

25. Satori

Satori provides many features that help govern data assets, fine-grain access usage, and classify data assets based on security classifications.

Key Strengths:

• Dynamic data masking and real-time access controls.

• Automated classification of sensitive data.

Ideal For: Organizations prioritizing data security and compliance.

Limitations: Users have reported challenges related to scalability and optimized cost, with some AI features in initial stages.

26. Anomalo

Anomalo is a data quality monitoring tool designed to identify data issues automatically, ensuring data reliability without manual setup.

Key Strengths:

• Automated anomaly detection using unsupervised machine learning.

• Real-time alerts and root-cause analysis for data issues.

• Integrates well with cloud data warehouses like Snowflake, BigQuery, Redshift, and Databricks.

Ideal For: Data teams that want proactive, automated monitoring of data quality without needing manual rule configuration.

Limitations:
Primarily focused on data quality monitoring, not a full-featured metadata catalog or governance suite. It can be complex to fine-tune for edge case scenarios.

27. SecuPi

SecuPi is a data security and privacy platform designed to protect sensitive data across analytics and operational system

Key Strengths:

• Specializes in fine-grained access control, dynamic data masking, and real-time data activity monitoring.

• Helps enforce data privacy and compliance (GDPR, HIPAA, CCPA) across data platforms.

• Integrates with major BI tools and databases without needing code changes.

Ideal For: Mid-sized organizations and enterprises focused on privacy, compliance, and fine-grain access controls.

Limitations:
More focused on data security and governance than traditional data cataloging or discovery. Users say it may require a dedicated team to manage and maintain policies across platforms.

Open source data catalogs

Open source data catalog platforms provide flexible and cost-effective metadata management options, driven by active communities and extensible architectures. They are well-suited for organizations willing to invest in customization and integration to meet evolving data governance and discovery requirements.

Tools under consideration:

• Apache Atlas

• Amundsen

• DataHub

• OpenMetadata

• Magda

• Egeria

• CKAN

• Marquez

28. Apache Atlas

Apache Atlas is an open-source metadata management and governance platform primarily designed for Hadoop ecosystems and big data environments.

Key Strengths:

• Strong data lineage, classification, and policy enforcement.

• Tight integration with Apache Hadoop ecosystem tools (Hive, Kafka, NiFi).

• Scalable and extensible for enterprise governance needs.

Ideal For: Organizations with Hadoop-based big data platforms requiring mature governance and compliance controls.

Limitations: Setup and maintenance complexity; UI is less modern; limited out-of-the-box connectors beyond Hadoop tools.

29. Amundsen

Amundsen is a data discovery and metadata engine developed by Lyft, emphasizing ease of use and search-driven discovery. 

Key Strengths:

• Simple, intuitive search interface for data discovery.Supports metadata ingestion

• from diverse data sources (Hive, Presto, BigQuery).

• Integration with authentication and access control systems.

Ideal For: Teams prioritizing fast data discovery and collaboration, especially in cloud or hybrid environments.

Limitations: Limited built-in governance and policy features; requires custom development for metadata ingestion pipelines.

30. DataHub

DataHub is an extensible metadata platform created by LinkedIn, designed for real-time metadata ingestion and rich lineage visualization.

Key Strengths:

• Graph-based metadata browsing and impact analysis.

• Fine-grained access controls and governance features.

• Growing ecosystem with support for many integrations.

Ideal For: Organizations seeking a scalable, extensible metadata platform with advanced lineage and governance capabilities.

Limitations: Deployment and maintenance can be complex, especially for smaller teams.

31. OpenMetadata

OpenMetadata is a community-driven metadata management platform providing automated ingestion, lineage tracking, and data quality monitoring.

Key Strengths:

• End-to-end metadata management with collaborative stewardship workflows.

• Automated metadata ingestion and lineage tracking.

• Role-based access control and policy enforcement.

Ideal For: Teams wanting a unified metadata platform with evolving community support and features.

Limitations: Relatively new project with ongoing feature development; smaller integration ecosystem compared to mature tools.

32. Magda

Magda is a federated open-source data catalog designed to integrate metadata from distributed and heterogeneous data sources.

Key Strengths:

• Plugin-based extensibility for diverse metadata sources.

• Focus on unifying metadata across complex data landscapes.

• Strong support for metadata federation and distributed governance.

Ideal For: Organizations with distributed data environments requiring a unified metadata catalog.

Limitations: Smaller community; requires technical resources for deployment and customization.

33. Egeria

Egeria is an open metadata and governance framework that supports metadata exchange and interoperability across various tools.

Key Strengths:

• Broad metadata type support and governance workflows.

• Open APIs for metadata sharing and integration.

• Supports interoperability between diverse metadata repositories.

Ideal For: Enterprises seeking an open standard framework for metadata governance and tool integration.

Limitations: More of a framework than a ready-to-use catalog; requires strong technical expertise to implement.

34. CKAN

CKAN is an open-source data portal platform widely used for publishing and managing open data, especially in government sectors.

Key Strengths:

• Robust dataset metadata management and API access.
• Public-facing data catalog with visualization capabilities.
• Active open data community and extensions ecosystem. 

Ideal For: Governments and organizations publishing open data for public access.

Limitations: Not designed for enterprise metadata governance or complex lineage tracking.

35. Marquez

Marquez is an open-source metadata service focused on pipeline metadata and data lineage, designed for data engineering observability.

Key Strengths:

• Integration with orchestration tools like Airflow and DBT.

• Real-time pipeline metadata capture and lineage visualization.

• API-first design for extensibility.

Ideal For: Data engineering teams focused on pipeline observability and lineage management.

Limitations: Limited features as a full enterprise data catalog; focused primarily on lineage and pipeline metadata.

Comparing the top 35 data catalogs

While most data catalogs offer a solid foundation, their capabilities vary significantly. Some excel in metadata management and lineage tracking, while others prioritize AI-powered automation or compliance support.

The table below provides a side-by-side comparison of the most critical features across leading data catalogs by segments

*Disclaimer: The segmentations and capability evaluations presented above are based on our independent research and product documentation as of May 2025. Features, pricing, and positioning may evolve over time. We recommend speaking directly with individual vendors for the most up-to-date and tailored information.

Key challenges in data catalogs across segments

After evaluating over 35 data catalog platforms across the Crawl, Curate, and Consume stages, distinct patterns emerged, not just in features, but in the real-world challenges that organizations are likely to face. Below are five core insights that reflect where current tools excel and where they commonly fall short.

Most tools prioritize one dimension, while only a few combine governance, usability, and comprehensiveness 

The current market shows a strong tendency toward specialization. Some platforms are designed for governance-heavy use cases with deep compliance features, while others focus on user-friendly collaboration, and some offer developer-first flexibility and extensibility.

However, very few platforms combine all three: governance readiness, business usability, and scalable metadata operations, in a unified experience.

This leaves many organizations either stitching together multiple tools or compromising on one front. The opportunity lies in delivering all three without trade-offs.

AI-powered discovery and self-serve capabilities are still maturing

Despite growing expectations around natural language interfaces and intelligent recommendations, self-service AI features are still early-stage in most tools. Many platforms claim AI capabilities but offer only basic semantic search or metadata suggestions, not true conversational querying or contextual recommendations.

This gap limits adoption by business users and slows efforts to democratize data access across non-technical teams.

Related Post: How an AI-Powered Data Catalog Transforms Discovery and Governance

Automation is standard, but rarely extends beyond data ingestion and lineage.

Metadata ingestion, schema discovery, and lineage mapping have become foundational capabilities, with most platforms offering automation in these areas. However, few tools extend automation to higher-value curation tasks such as suggesting glossary terms, classifying business-critical assets, or surfacing anomalies in governance coverage. While ingestion is well-covered, the automation of user action remains limited.

Metadata curation still relies too heavily on manual effort

Few tools provide built-in mechanisms to help teams prioritize what metadata to curate or delegate ownership effectively. Features like importance scoring, usage-based ranking, and automated stewardship assignment are inconsistently supported. Without them, teams often struggle to scale governance beyond a handful of critical assets.

The result: large volumes of metadata that remain uncataloged, unowned, or underutilized.

Extensibility comes at the cost of governance readiness

Tools that offer deep customization and API-driven extensibility often do so at the expense of out-of-the-box governance functionality. While they provide flexibility to adapt to unique architectures, they typically lack prebuilt workflows for policy enforcement, compliance tracking, or cross-functional stewardship.

This forces teams to build governance frameworks from scratch, which slows time-to-value and increases implementation risk.

How to choose the right tool for your team

Choosing a data catalog in 2025 isn’t just about features. Many tools offer similar checklists, like metadata scanning, lineage, and policy enforcement, but their real value lies in how well they match your team’s data maturity, structure, and growth.

1. Understand your data maturity

Most teams span multiple stages, connecting diverse sources, organizing metadata, and enabling self-service. Identify where your biggest gaps are today:

• Struggling to inventory your ecosystem? Prioritize tools with deep, native connectors (databases, SaaS, ETL, BI, files). These speed up onboarding and surface richer metadata.

  Modern and legacy vendors often excel here.
  

• Manual governance slowing you down? Look for AI-powered automation, lineage detection, PII classification, and business glossary enrichment.

  Legacy vendors offer depth; modern tools offer agility.
  

• Poor adoption among business users? Focus on tools with intuitive UX, contextual guidance, and built-in collaboration.

  These drive cross-team adoption.

2. Identify key user personas

Effective catalogs support engineers, analysts, and governance teams alike:

• Data engineers need lineage, schema change tracking, and DevOps integrations.

• Business users want trusted definitions, search, and simple access workflows.

• Governance roles require audit trails, policy controls, and safeguards for collaboration.

Choose a tool with role-based experiences. Many modern platforms are closing the gap here with collaborative design and embedded governance.

3. Define your specific use cases

Before comparing vendors, clarify the problems you expect the tool to solve:

• Do you need end-to-end lineage across pipelines?

• Are you governing PII or financial data?

• Will stewards manage glossaries and data quality at scale?

• Do you need decentralized access controls?

These questions move you beyond checklists and reveal how vendors approach implementation. Some specialize in lineage, others in workflows or collaboration.

4. Evaluate integration depth & extensibility

Catalogs are only as useful as the metadata they extract and how well they fit your stack.

• Some tools pull only technical metadata; others go deeper, usage stats, classifications, access policies.

• Assess integrations with key systems like Snowflake, Power BI, Salesforce, and AI platforms.

Legacy and modern tools often offer deeper, prebuilt connectors. Open-source options may need custom setups.

5. Balance scalability, cost, and complexity

Avoid overbuying. A team of five with one data warehouse has different needs than a multinational bank.

• Smaller teams benefit from lightweight tools with fast deployment and built-in automation.

• Growing teams need scalable workflows and metadata unification.

• Highly regulated orgs may require advanced auditability and policy enforcement, areas where enterprise tools shine.

Don’t just think about your immediate needs. Evaluate whether the vendor can support broader use cases as your maturity evolves, without introducing unnecessary cost or operational complexity.

Related Post: 3 Pitfalls to Avoid When Choosing a Data Catalog

Final thoughts: Go beyond a features checklist

While feature lists provide a snapshot of a tool's capabilities, they don't capture the full picture. It's essential to consider how a data catalog tool aligns with your organization's workflows, user needs, and long-term data strategy. Engage stakeholders from various departments, conduct thorough evaluations, and prioritize tools that offer flexibility, scalability, and user-centric design.

Frequently asked questions (FAQs)

1. What is a data catalog tool and why is it important?

A data catalog tool helps organizations inventory, classify, and manage their data assets to improve data discovery, governance, and usability. It’s essential for enabling data-driven decision-making and ensuring compliance.

2. Do data catalogs replace data governance platforms?

No. Data catalogs are components of a broader governance ecosystem. Platforms like OvalEdge combine cataloging with stewardship workflows, policy enforcement, and compliance reporting.

3. Are data catalogs useful without a data warehouse or data lake?

Yes. Modern data catalogs connect to BI tools, SaaS apps, APIs, and file systems, making them valuable in cloud-native or distributed environments.

4. How do I choose the right data catalog tool for my organization?

It depends on your data maturity, team roles, integration needs, and budget. Clarify whether you need more focus on discovery, governance, or consumption, and whether legacy enterprise, modern, or open-source tools are the best fit.

5. How to choose the right data catalog for my team size and maturity?

Start by assessing your team's data maturity.  Modern tools like Atlan work well for growing teams. Legacy enterprise tools suit large, compliance-heavy environments. Open-source options are great for tech-savvy teams seeking customization.

6. What are the key differences between legacy enterprise and modern data catalog tools?

Legacy tools offer deep integrations and compliance features but are often costlier and more complex.  Modern tools prioritize agility, ease of use, and faster time-to-value.

7. Are open-source data catalog tools reliable for enterprise use?

Yes, tools like DataHub and OpenMetadata offer flexibility and control, but they typically require more engineering resources for setup and maintenance.

8. What’s the best open-source data catalog?

DataHub and OpenMetadata lead in open-source adoption and extensibility, backed by strong community support.

9. Are legacy enterprise tools still relevant in 2025?

Definitely. Large enterprises still depend on them for robust compliance features, wide connector coverage, and proven governance workflows.

10. Is Atlan better than Alation?

They serve similar mid-market audiences. Atlan stands out for user experience and collaboration, while Alation excels in governance maturity and search intelligence.

11. How important are connectors and integrations in a data catalog tool?

Extremely important. Broad and deep connector support ensures your catalog captures timely metadata across all systems, driving data visibility and trust.

12. Can data catalog tools help with data governance and compliance?

Yes. Features like lineage, classification, policy enforcement, and audit trails help align with enterprise governance and regulatory standards.

13. What role does AI and automation play in modern data catalog tools?

AI and automation reduce manual effort by streamlining metadata tagging, lineage tracking, and data quality checks, boosting adoption and accuracy.

14. Can data catalogs integrate with AI tools?

Yes. Many catalog tools now integrate with machine learning (ML) platforms and registries to surface model metadata, features, and training datasets.

15. What is the best data catalog for a startup?

Tools like Select Star and Secoda offer fast onboarding, automation, and affordability, making them ideal for data-driven startups.

16. Which data catalogs offer the best UI/UX for business users?

Atlan, Castor, and Metaphor are known for clean interfaces, contextual search, and guided onboarding tailored to business personas.