With businesses today managing vast volumes of data spread across various databases, cloud services, and Internet of Things (IoT) devices, keeping track of everything is becoming increasingly challenging. This also makes it tough for data teams to track down the information they need and make sense of it all.

That’s where data catalogs help. Unlike in the past, when data catalogs needed custom scripts to search for and gather information, today's systems are much more advanced. They can automatically detect and organize data properties and profiles, making life a lot easier.

Stick around to discover how data catalogs can transform your data management and get a closer look at 10 leading data catalog tools. 

What is a data catalog tool?

A data catalog is an organized inventory of metadata designed to help businesses efficiently manage and locate their data assets. Acting as a central repository, it lists, describes, and categorizes all available data within an organization, offering users a comprehensive view of their data resources.

By leveraging metadata—details about the data itself—a data catalog enhances understanding of data context, sources, and usage. It enables professionals to collect, organize, and enrich metadata to support data discovery and governance, making it easier to access, analyze, and utilize information effectively.

How to choose the right data catalog tool for your company

When evaluating a data catalog tool, there are several factors you should consider. Here are some steps to take to choose the best tool for your business:

• Consider Your Needs — First, it can be helpful to clearly outline the needs and goals of your business and what you expect from your data catalog tool to help you achieve these goals. Talking to your data team and other decision stakeholders can help you outline these goals. Once you have your goals in mind, it will be easier to narrow down the selection of potential tools.
• Compare Tools — Next, you can compare the different data catalog tools. Take a look at the different features and see how they can help you reach your data goals. See if the tools have capabilities outside of data cataloging and if those features are important to you. You should also keep your budget in mind, as the price of each tool can vary.
• Seek Feedback — Once you’ve lined up a few options, it’s good to seek some second opinions. Talk to your data team and other team members to get feedback about each of your options. This could help you narrow down your options even more before you start trying tools out. It’s also a good idea to ask peers in your industry about their experience with data catalog tools and if they have any recommendations.
• Demo Your Tool — Now you’re ready to give the tools you’ve selected a trial run. Most companies offer free trials or demos to show you how their tool could help your organization. Make sure your team members can test these tools too. This allows you to get a comprehensive picture of each option and see if your team has any favorites. 

Choosing the right data catalog tool is important, so make sure to weigh your needs and goals when making your decision.

Key features of a data catalog tool

The key features of a data catalog tool include:

1. Data Integration and Discovery: The ability to connect to various data sources and automatically discover metadata from systems, applications, databases, files, and external APIs. Continuous data discovery is essential to keep the catalog up to date.

2. Search and Find Functionality: A data catalog should provide robust search capabilities, allowing users to find and access relevant data efficiently. This includes full-text search powered by Artificial Intelligence (AI) and Large Language Models (LLMs).

3. Metadata Management & Templates: Good data catalogs allow adding custom metadata and tagging data with information, such as data categories, business owners, and other important details.

4. Data Lineage: Data lineage helps users understand the origin and destination of any data asset in a data catalog. It is essential to meet regulatory requirements for the traceability of calculations and data preparation.

5. Business Glossary: A business glossary is crucial for linking technical metadata to business terms, providing a common understanding of the organization's data assets.

6. AI and Machine Learning: Many modern data catalogs use AI and machine learning algorithms for metadata harvesting, cataloging, tagging, and automated search recommendations.

7. Collaboration Features: Collaboration tools, such as the ability to rate a dataset, comment on it, share it with coworkers, and other social features are important for user adoption.

8. User Access Controls: The catalog tool should allow you to set granular role-based access controls (RBAC) to ensure data governance.

9. Automation: Automation through AI ensures that the catalog is always up to date, as manual maintenance can lead to obsolete data and decreased user adoption.

10. User Experience (UX): The catalog should be designed and accessible for users of all technical backgrounds, with advanced features reserved for technical users, such as data stewards and engineers. Usability is a key factor for long-term user adoption across the business.

11. Data Monitoring and Observability: Rather than having a separate tool and creating additional costs and platforms to manage, choose a tool that provides more than just data cataloging. The most comprehensive tools integrate data monitoring into the catalog to give you visibility into the health of your entire stack in one place.

Why are data catalog tools useful?

Effective data catalogs are more than just a repository of definitions and assets. For data catalogs to be valuable, they need to be user-friendly for both technical and non-technical users.

Data catalog tools are useful for several reasons, including improved data efficiency and analysis. 

Here are some key benefits of using a data catalog tool:

• Improved Data Efficiency: Data catalogs make it easier to find data, avoid duplication, understand data better, ensure data rules are followed, make data integration smoother, and encourage teamwork.

• Enhanced Data Context: Data catalogs provide detailed information about datasets, including their origin, quality, usage, and connections to other datasets. This information makes it easier for users to understand the meaning, importance, and suitability of the data, leading to better decision-making and analysis.

• Streamlined Data Access: A well-designed data catalog makes it easier for users to find the right datasets and build and launch new use cases. It also helps quickly identify areas for improvement in their data management strategy.

• Improved Data Analysis: Data catalogs improve data analysis by making it easier to find data, offering context with metadata, ensuring data quality, and enabling teamwork.

• Increased User Trust: By providing important information about each dataset, data catalogs help ensure that users have the correct data for their applications, thus increasing user trust

Overall, data catalogs play an important role in enabling organizations to make better use of their data and drive insights that can help inform business decisions.

Top 10 Data Catalog Tools Comparison

1. Secoda

Secoda is a data management platform that leverages AI to transform data cataloging, making it easier for users to manage and access their data. Its AI-powered search allows users to find and interpret metadata without the need for extensive technical knowledge.

Secoda makes your data easily searchable for all users in one collaborative and intuitive platform. Since the beginning, Secoda has been dedicated to democratizing access to data knowledge at scale. We’ve built AI solutions that allow any employee to easily search, understand, and use company data, regardless of their technical ability. This feature is essential for data-driven organizations that require fast, accurate, and intuitive search capabilities to navigate their vast data landscapes efficiently.

With the most powerful and comprehensive search, Secoda enhances everyone's ability to quickly find the data they need.

Key features

• No-code data centralization on a highly scalable platform.
• Visualizes data relationships with automated lineage functionality.
• Out-of-the-box integrations with business intelligence (BI) tools, data warehouse tools, and more. 
• Automated metadata ingestion collects metadata from across your data stack. 
• Integration with Git facilitates version control and collaboration on data changes. 
• Role-based permissions allow admissions to control data access based on user roles. 
• AI-powered search provides contextual answers to data inquiries and is accessible to all users.

Pros

• Advanced AI integration
• Real-time collaboration 
• Self-service analytics for nontechnical users
• SOC 2 compliant secure data management platform 
• Modern, intuitive design with a user-friendly interface

2. Collibra

Collibra offers a Data Intelligence Cloud platform centered around its Data Catalog. It helps users discover and categorize data automatically with a machine learning algorithm. It also includes features like data curation and data lineage tracking, which are also supported by machine learning.

Collibra can also manage metadata with graphs so users can better understand data quality and lineage. It can also automate the process of data discovery and curation across data sources for accurate insights. 

Key features

• A centralized repository for business terms to promote a common understanding across the company.
• Artificial intelligence to label and categorize data so it is easier to find.
• Data managers and users can collaborate using built-in chat boards and alerts.
• Collibra helps companies follow data rules like the General Data Protection Regulation (GDPR).

Pros

• Strong integration capabilities 
• Robust governance features that enhance data quality
• Active community and support resources for users

Cons

• Time-consuming initial setup and configuration
• Lacks automated lineage and workflows
• Lacks intuitive data visualization and reporting for users
• Lacks AI-assisted search and advanced analytics capabilities
• Less mature data quality functions like observability, security, and connectivity

See a full comparison of Secoda vs Collibra

3. Stemma

Stemma is a fully managed data catalog solution based on the open-source Amundsen platform, which Teradata acquired in 2023. It offers reliable metadata management and enterprise features. Stemma is ideal for companies that want to promote a self-serve data culture. It can also enrich metadata automatically and customize user data experiences. 

Stemma can integrate with major data sources like Snowflake, Redshift, and BigQuery. It can improve data discovery and documentation, leading to better team communication. 

Key features

• Real-time collaboration for multiple projects.
• Data customization for businesses to meet their specific business needs.
• Stemma can explain the relationships between data assets with a knowledge graph for better understanding.
• It can collect and save data from different sources automatically.

Pros

• Enterprise-grade security
• Personalized user experience
• Better data discovery and accessibility
• Integrates smoothly with existing data infrastructure

Cons

• Limited offline capabilities
• Costs can be high for small companies 
• Limited technical documentation and demos
• Lacks advanced data governance and visualization features
• Integration challenges with certain legacy or uncommon data systems

4. SelectStar

SelectStar is an intelligent data discovery platform. It simplifies data governance and management with automatic data cataloging and usage insights. The tool is designed to help teams find and trust their data. 

SelectStar can analyze and display column-level data lineage. This process makes sure that users always know where data comes from and how it is being used. Its user-friendly interface allows both technical and non-technical stakeholders to engage with data efficiently. 

Key features

• Monitors and alerts users to any changes in data schemas to reduce the risk of errors.
• Allows users to search across all data sources to prioritize results based on popularity and relevance.
• Generates and maintains up-to-date documentation for data assets to reduce manual effort and improve accuracy.
• Provides granular control over data access so only authorized users can view or modify sensitive data.
• Easily connects with popular BI tools to enhance data accessibility for reporting and analysis.

Pros

• Better data quality and governance
• Quick setup and deployment within an hour
• Highly automated processes and efficient documentation

Cons

• Lacks AI powered search and workflow functionality
• Higher cost for premium features
• Limited functionality for data quality
• Semantic search and version control with GIT are not available
• Lacks the ability to generate documentation from metadata automatically

5. Alation

Alation is another popular legacy data catalog and data intelligence platform. It makes it simple for companies to find and trust their data.

Alation is the foundation for data intelligence, centralizing data context and policies. This helps create a single source of truth and streamline data management. Alation also offers a simple interface with collaborative features that connect data stewards and business users.  

Key features

• Users can build wiki pages, searchable chats, and subscribe to data updates.
• Customizable reporting and data insight are available through the Alation Cloud Service.
• Helps with the creation of custom connectors to integrate with various data sources.
• AI-powered SQL editor helps with queries and flags deprecated data to ensure accuracy.

Pros

• User-friendly interface
• Centralized data context
• Flexible and scalable for enterprise
• Prebuilt connectors simplify integration 

Cons

• Limited AI/ML functionality
• Costly for smaller organizations
• Insufficient curation progress tracking and analytics
• Customization options may be limited compared to open-source alternatives
• Closed legacy platform, unable to support federated or distributed models like Data Mesh and Data Fabric

6. Castor

Castor is an automated data catalog tool. Its main purpose is to improve data documentation and organize it for easy access. It uses AI to generate  descriptions for data assets so teams can easily access and understand their data. 

Castor centralizes documentation from various sources for a unified repository. This promotes collaboration and transparency across the company. Unlike traditional data catalogs, Castor integrates conversational AI to make it easy for users to interact with data intuitively and retrieve insights with natural language queries.

Key features

• Automates the creation and updating of data documentation.
• Sends real-time updates and notifications via Slack.
• Connects key business metrics directly to relevant data assets.
• Organizes data using simple tags and categories.

Pros

• User-friendly interface
• Automated syncing keeps documents update
• Make data understandable 
• Better compliance with regulations 

Cons

• Vague data quality processes
• Limited functionality for integrations 
• Data monitoring and observability not available
• Doesn’t offer data quality tools for quality checks

7. Atlan

Atlan is a traditional data catalog. Atlan enables easy and intuitive self-service analytics through its data discovery tools. Atlan also offers tools for data lineage, data governance and more.

Key features

• Natural language search to find data using keywords and related synonyms.
• Business context search to discover assets linked to specific business metrics.
• Custom filters based on metadata properties for a tailored search. 
• Companion search to validate data choices.

Pros

• Decent and intuitive user interface 
• Good customer support team
• Vast tech stack connectivity

Cons 

• Expensive contracts starting at over $100K
• Initial learning curve
• Expensive for what you get
• Lacks integrated data quality modules
• AI-driven insights require additional costs

8. Amundsen

Amundsen is an open-source platform developed by Lyft that simplifies data discovery and management for teams. It is used by analysts, data scientists, and data engineers who need quick access to reliable data. 

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For analysts and data scientists, Amundsen offers an easy-to-use search feature to find and understand data. It contributes to the breakdown of barriers between various data sources. Amundsen automatically refreshes data context for data engineers and software engineers, reducing disruptions and ensuring the correct data is used.

Key features

• Detailed metadata like user stats and last updated information.
• Simple search to find data quickly and recommendations based on names, descriptions, and activity. 
• See frequently used data, ownership, and common queries, helping to understand and leverage shared knowledge.
• Context sharing to update table and column descriptions, minimizing confusion and unnecessary back-and-forth communication.

Pros

• Easy to use and integrate
• Straightforward metadata ingestion
• Quick setup for Docker, EC2, and Kubernetes

Cons 

• Less active community 
• No granular access control
• Versioning system unavailable
• Doesn’t offer built-in data quality and data governance features
• Lesser advanced features compared to other commercial tools

9. Apache Atlas

Apache Atlas is an extensible governance platform designed for Hadoop ecosystems. It provides metadata management and data governance capabilities. Through integrations with various enterprise systems, it enables organizations to build and maintain a data catalog, classify assets, and ensure compliance, making it easier for teams to collaborate on data governance.

Apache Atlas is an extensible governance platform designed for Hadoop ecosystems. It provides metadata management and data governance capabilities. Through integrations with various enterprise systems, it enables organizations to build and maintain a data catalog, classify assets, and ensure compliance, making it easier for teams to collaborate on data governance.

Key features

• Support for predefined and custom metadata types
• Dynamic classifications for data, including PII and data quality
• REST APIs for seamless integration and management
• Intuitive UI for data lineage tracking across processes
• Fine-grained metadata access control through Apache Ranger

Pros

• User-friendly UI
• Scalable for large enterprise environments
• Customizable metadata management
• Integration with Apache Ranger for security and data masking

Cons 

• Slow response time
• Lacks data governance tools
• Laborious and resource-intensive initial setup and learning processes
• Significant configuration required to meet specific enterprise requirements.

10. Data.world

Data.world is a comprehensive data catalog platform that accelerates data discovery and fosters collaboration using AI-driven tools like Archie Bots. The platform enables users to quickly search, explore, and manage data.

Key features

• AI-assisted search with deep contextual results
• Natural-language interaction for data queries
• Auto-enrichment of data and metadata with Archie Bots
• Knowledge-graph powered search for deeper exploration

Pros

• Interactive and easy-to-use UI
• Easy download and integration 
• Enhances decision-making through AI-driven insights
• Empowers non-technical users with self-service data discovery

Cons 

• Limited documentation and delayed customer support
• Graphs and charts could be more advanced and visually intuitive
• Steep learning curve for new users, requiring additional training
• Lacks diversity and may not stay updated with the latest technologies
• Insufficient data availability for specific use cases like hyperspectral images

Enhance your organization’s data discovery with Secoda

Organizations looking for an all-in-one data management and data discovery tool should choose Secoda. Secoda has the data catalog features you need to organize and centralize your data, along with numerous tools for other aspects of data management. Businesses that want to easily enable self-service analytics in their organization can trust Secoda to get them there.

Why Choose Secoda?

• Centralized Data Management: Keep all your data in one place for easy access and organization.
• Enhanced Data Discovery: Quickly find the data with user-friendly search features.
• Self-Service Analytics: Enable your team to perform analytics independently without relying on others.
• Automated Metadata: Automatically manage and update metadata to keep information accurate and current.

‍Try Secoda today to see if our platform is right for you.