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Explore the best data catalog tools and find out their pros, cons, specifications and more. Discover the top data catalog tool for your organization here.
Providing your employees access to timely, reliable, contextualized data creates a major competitive advantage for your company.
Yet the ever-increasing volume and diversity of enterprise data create persistent challenges for data leaders in managing data assets and helping their teams make sense of it.
Data catalogs are tools that make organizing, managing, and discovering data assets easier and allow your teams to understand and leverage data assets faster.
In this article, we break down the key features, benefits, and drawbacks of the top data catalog tools to help you identify the right tool for your organization's needs.
Data catalog tools provide a unifying layer that sits atop enterprise data to support data governance, access, discovery, lineage, and many other capabilities.
Data catalog tools allow organizations to manage metadata (data about data), such as keywords, data sources, data types, and other attributes. This enables you to enrich and contextualize your data, helps you organize and govern data, and allows technical and non-technical users to easily discover and use data assets relevant to their use cases.
Unlike traditional data catalog solutions, data.world’s catalog is powered by knowledge graph architecture, enabling companies to store and represent their data as nodes on a graph, establish relationships between data, and build a holistic view of their data ecosystem.
AI-assisted search of data assets means you reduce the risk of human error. Knowledge graphs build the rich context to generate accurate, explainable responses from LLMs at scale – responses you can directly trace back to your enterprise knowledge.
Knowledge Graph Catalog: Easily map data context, meaning, and relationships into a single view using a differential graph database
Consumer Data Apps: Build configurable data apps for specific users, roles, use cases, and workflows
Intelligent Data Discovery: Use AI to get the most context-specific, relevant data for your needs while easily filtering out unimportant data
Self-Service Data for Business Users: User-friendly experience that allows non-technical users to independently discover, comprehend, and use data
Federated Querying: Interconnect various data domains by querying across different sources to eliminate data silos and provide a unified view of organizational data
Learning Care Group leveraged data.world’s intuitive interface to streamline their data search and analysis processes. A senior manager from the company shared, “On our journey toward self-service analytics, data.world has made us a stronger company. By empowering our team with deeper insights, we're creating opportunities for growth and development, helping everyone understand who we are as an organization.
In data.world’s reviews, users discussed features, benefits, and drawbacks of data.world. Once user noted, "The platform is user-friendly. Not only can we search the dataset, but also the organizations related to the keywords." Another said, "It needs to display the starting price and a search bar on the very first page that opens, saves time for the users."
Pros
Knowledge graph approach captures rich data context and relationships
Enables data certification processes to validate trusted assets
Platform extensibility using APIs and custom data apps
Leverages an AI intelligent search that improves with use, enhancing data discovery by surfacing increasingly intelligent and relevant results
Provides guided ideation to simplify data exploration for non-technical users
Cons
SQL knowledge unlocks feature accessibility, so may be more limited for those who don't know SQL
data.world offers straightforward, flexible pricing options, including Essentials, Standard, Enterprise, and Enterprise+ plans. Users can reach out to the data.world team to create a tailored package that suits their specific requirements.
To learn how data.world can help your teams find the data they need 10x faster, book a free demo today.
Alation is a data intelligence platform that focuses on creating a collaborative environment for data discovery and governance. Alation’s data catalog is searchable and easy to navigate, aiming to bridge the gap between technical and non-technical business users in understanding data assets and helping foster a data-informed organizational culture.
Alation can be pricey for smaller companies, as it caters more to larger, more traditional enterprises looking to enhance collaboration and breakdown data silos. While the platform focuses more on data discovery than governance features, its automated governance systems can be especially useful for large companies with extensive data ecosystems.
Policy Center: Provides a centralized view of data policies, standards, and rules to simplify auditing and governance
Metadata Management: Enriches data fabric with behavior-driven metadata from multiple sources
Behavioral Analysis: Delivers surface-level insights like data popularity, relevance, and recommendations based on data usage
ML Model Cataloging: Catalogs machine learning data, models, and notebooks
Data Quality & Trust: Ability to set trust flags, visualize data quality metrics, and monitor data from one location
Automated Data Discovery: Automates data discovery and classification of sensitive data to reduce the risk of human error
Alation’s reviews show what users had to say about features, benefits, and drawbacks. One user noted, "Alation is extremely flexible and able to fit small to large organizations. They tread the perfect line between analytics and governance. Their vision perfectly aligns with our organization's data management vision of universally understood and trusted data available across the company." They also said, "There is definitely some room for growth with lineage capabilities."
Pros
Enables a collaborative and transparent approach to data governance
Curated and contextual metadata enhances data access and discovery
Provides simple governance systems for machine learning
Supports cloud data migration using dependency mapping
Allows for easy integration of other data sources and tools
Uses AI and machine learning to automate repetitive data governance tasks
Supports hybrid deployments of your data across cloud and on-site storage
Cons
Limited scalability for centralized or federated data architectures
Lineage and workflow functionality costs extra
Lacks powerful automation and AI tools compared to other data catalog solutions
Lacks comprehensive support and UX fixes
Some sources note that Alation's pricing starts at $60,000 for one year (per the AWS Marketplace). Another sources notes that for an organization with 300 Alation users, the annual cost would be around $246,000, escalating in the second and third year (per The Total Economic Impact of the Alation Data Catalog).
Reportedly, Alation subscriptions vary by user count and license type, which are listed as: Contributors, Consumers, and Collaborators.
Collibra is an enterprise-level data intelligence platform that provides comprehensive data governance, quality, and privacy solutions. The platform offers automated compliance capabilities and tools to establish and enforce data policies and standards that mitigate regulatory risk. Their metadata management capabilities allow users to enrich data with qualitative information that enhances understanding and use.
Similar to Alation and data.world, Collibra simplifies data stewardship workflows, policy management, and sensitive data classification at scale. However, Collibra’s price can be prohibitive for smaller companies, making it a solution more tailored to larger enterprises.
Smart Data Catalog: Automatically classifies and contextualizes data assets by connecting business, technical, and privacy metadata
Data Observability: Monitors data quality, pipelines, and validation rules across 40 data sources
Automated Data Lineage: Automatically maps end-to-end data lineage and provides metadata details
Data Access Management: Leverage metadata context to enforce data access policies
Automated Data Classification: Uses specific algorithms to classify sensitive data based on your business needs
Collibra’s customer reviews report a mixture of positive and negative feedback about the tool. One user exclaimed, "With Collibra, we loved the fast and pain free implementation using Collibra's professional services, the intuitive user interface, the ultimate flexibilty for our data mesh and data domain framework, licensing model, and expandability with the Data Quality module."
On the less positive side, one user went into detail about the downsides of Collibra. They said, "Learning Curve: the flexibility that makes Collibra stand out can also be a double-edged sword. The initial learning curve for both users and deployment teams can be steep. Understanding how to harness the full potential of Collibra's features might require investing a significant amount of time in training and familiarization.
Potential for Confusion: With great flexibility comes the potential for confusion. The plethora of options and customizable features might overwhelm new users. It's essential for organizations to establish clear governance and training strategies to ensure users make the most of the tool without getting lost in its complexities. It is good to highlight that despite the complexity, Collibra uses really basic concepts that, once learned, speed up the implementation curve.
Deployment Challenges: Deploying Collibra across an organization can sometimes pose challenges due to its highly customizable nature. Ensuring that the tool aligns perfectly with existing processes and workflows might demand careful planning and customization, possibly adding some complexity to the deployment phase."
Pros
A comprehensive platform that provides governance, cataloging, lineage, and observability solutions
Establishes a strong, centralized data governance model
Robust workflow automation for data governance and stewardship processes
Powerful data classification and sensitive data detection
Extensive connectivity across a wide range of data sources
Cons
Steep learning curve and challenging implementation process
Lacks advanced AI and machine learning capabilities for search functions compared to other tools on the market
Cost-prohibitive for smaller organizations
Provides limited support for custom builds or configurations
On AWS Marketplace, they note that a Collibra subscription package starts at $170,000 for 12 months.
Coginiti provides a centralized workspace to capture, govern, and reuse an organization's analytics models and data assets. With built-in version control, security controls, and the ability to work across multiple data sources, Coginiti empowers teams to efficiently build upon existing organizational knowledge.
The platform's collaboration capabilities enhance transparency and productivity by easing search and discovery across different data domains. Coginiti’s platform offers a more affordable solution catered to smaller organizations.
Smart Data Catalog: Automatically classifies and contextualizes data assets by connecting business, Team Analytics Catalog: Provides a central catalog for all your data models, schemas, insights, and code
Version Management: Allows you to easily manage and sync changes across different analytics versions
Data Exploration: Enables simple exploration and analysis of data across different sources and platforms
Data Modeling: Allows you to easily create, share, and document data models
Centralized Security: Enables granular security controls and permissions for data assets
Knowledge Sharing: Encourages collaboration by making code visible and useable to all stakeholders
Coginiti’s customer reviews go over some of the benefits and drawbacks of the tool. On the plus side, customers say: "Coginiti is easy to create connection with my work cloud accounts. They help you write your code and review it easily with color coding. The product developers keep on going improving Cogniti, and are willing to teach you for free via frequent webinars."
On a less positive note, other users mentioned that they don't support MongoDB, and that trainings/guides should be more easily accessible for the new folks using the tool.
Pros
Increases team productivity by providing a centralized analytics knowledge base.
Enables simple discovery and reuse of an organization's data assets.
Streamlines data warehouse design, documentation, and maintenance.
Allows simultaneous work across multiple data platforms and sources.
Establishes a centralized security model for all data analytics governance.
Cons
Relatively new and less mature compared to established vendors.
Limited out-of-the-box integrations with some data sources.
No native data quality, observability, or governance capabilities.
Lacks customizable settings, sometimes requiring additional tools to meet data management needs.
Coginiti offers three tiers of pricing: Coginiti Pro, Coginiti Team, and Coginiti Enterprise. For Team and Enterprise, you'll need to contact the sales team for pricing. For the more basic "Pro" plan, it starts at $189 per user annually.
Denodo's data virtualization platform enables organizations to create a unified data fabric that removes the complexities of underlying data sources. Denodo stands out by providing high-performance data integration across various data stores using smart query optimization and advanced semantics.
Denodo's data services layer provides centralized governance and access control while also allowing for distributed data domain architectures. However, Denodo can be expensive for smaller organizations.
Logical Data Abstraction: Represents all your data assets in a decoupled, virtualized layer, independent of any source systems
Smart Query Acceleration: Leverages AI, caching, and optimization techniques to deliver top query performance
Data Services: Allows publishing and sharing of virtualized data using SQL, REST, JSON, and GraphQL APIs
Flexible Data Integration: Easily integrates 200+ data source types using pre-built connectors. Supports real-time federation, caching, replication, streaming, and other data integrations
Unified Data Governance: Centrally manage and configure security policies, access controls, and auditing across data assets
Denodo’s customer reviews note some of the features and drawbacks of the tool. One user noted, "What I highlight the most is its ability to bring together dispersed and heterogeneous data sources into a unified and coherent view, without the need to physically move the data."
Another user said, "Some users might find the learning curve steep, especially when dealing with complex data integration scenarios." A few other people mentioned that the user interface could be easier to navigate.
Pros
Provides a logical data layer that sits atop underlying data sources
Offers simple connectivity options for integrating diverse data
Leverages AI and machine learning to provide smart querying and recommendations
Facilitates data sharing with multiple interfaces and APIs
Establishes centralized governance, security, and access controls
Cons
Primarily query-oriented; lacks full automation of data governance operations
Performance can degrade for highly complex data processing workloads
Can be costly at very large scales
Initial integration is difficult and time-consuming and can lead to vendor lock-in over time
Denodo pricing isn't listed on the website, but pricing tiers are as follows:
Denodo Professional: Supports 5 data sources, for small single use-case projects within individual departments
Denodo Standard: for multiple use cases within individual departments
Denodo Enterprise: Enterprise-wide deployment for multiple use cases and groups and large data volumes
Denodo Enterprise Plus: Comprehensive collaboration and automation, plus advanced security for enterprise-wide deployments
Informatica's Enterprise Data Catalog uses AI and machine learning to automate metadata management and contextual data enrichment for large and complicated data environments.
The platform easily handles enterprise-scale metadata, making it a popular solution for larger organizations, and its pricing model tends to be more affordable than comparable enterprise catalog options. The catalog also integrates with the platform's data integration, data quality, and governance offerings.
Contextual Data: Provides data lineage, profiling, and quality analysis across your entire data ecosystem
Automated Data Cataloging: Uses AI scanners that can automatically index your metadata across the cloud
End-to-End Data Lineage: Tracks data movement and transformations from external integrations and pipelines to its final destination
Axon Data Governance Integration: Connects with Informatica's Axon Data Governance solution, simplifying your data governance processes
Data Collaboration: Eliminates data silos and enables company-wide data sharing
Intelligent Data Curation: Uses AI to run data similarity analyses, business term associations, and automated annotations to reduce the risk of human error and increase data visibility
Informatica’s customer reviews cover some of the drawbacks and benefits of using the tool. One user liked "its ability to create comprehensive catalog of enterprise data, making it easier to understand, manage and govern data assets." Many users also said that it is easy to use and implementation is straightforward.
On the less positive side, users noted that it is still evolving as a product and Support could do a better job of communicating. One user complained, "Needs work on the visibility of metadata management and data lineage capabilities. It is also pretty time-consuming for beginners to understand the UI and get started."
Pros
Allows for enterprise-scale metadata scanning and cataloging.
Provides rich context through data lineage, profiling, and quality views.
Uses AI to automate metadata curation and annotation.
Supports metadata extraction from various source systems.
Facilitates data collaboration and transparency across teams.
Supports multi-cloud data integration at scale.
Enables data privacy and protection of company policies and processes.
Cons
Limited in customization and visualization options.
It can be expensive for broader deployments beyond mere cataloging.
Provides a heavy focus on metadata but lacks some automation capabilities compared to other cataloging tools.
Requires additional integration efforts for external data sharing.
Informatica products utilize IPU-based (Informatica Processing Unit) pricing, though the exact nature of an IPU—its compute and storage capacity—remains somewhat ambiguous. A clue is found in the AWS Marketplace listing for Informatica Intelligent Data Management Cloud, which lists 120 IPUs at an annual cost of $129,600.
On the AWS Marketplace, running Informatica for up to 50 metadata resources costs $100,000 per year. Another source indicates that a subscription for 100 users starts at $150,000 annually. CDW cites licensing fees for 1,800 metadata resources at $531,149.99. These fees cover only the licensing costs and exclude cloud computing or infrastructure.
Informatica doesn't list pricing on its own website. The available data suggests pricing ranges from hundreds of thousands to over half a million dollars.
Talend’s data catalog provides a centralized point of control for your entire data ecosystem and data governance systems, which allows for better security and collaboration across your organization. The platform can extract metadata from diverse sources and automatically profile, organize, and enrich it. The catalog also connects to the company's larger product suite, which includes data quality, integration, and analytics tools. Talend’s pricing model is based on the number of yearly users, making it more affordable for smaller companies with fewer users.
Automated Data Discovery: Uses machine learning to automatically document, enrich, and create smart relationships with your metadata
Multi-Cloud Support: Provides seamless storage and integration across cloud data warehouses such as Snowflake, Azure, AWS, Google Cloud, etc.
Pervasive Data Lineage: Creates an end-to-end view of technical data lineage and provides mapping visualizations
Collaborative Contribution: Enables crowd-sourcing of metadata, business glossary information, and data certifications to collectively improve data quality
Automated Data Prep: Securely integrates data sources while extracting relevant, technical, and business metadata
Talend’s user reviews go into more detail about key features, benefits, and drawbacks. Some users noted that task scheduling and administrative tasks are easy. One user said, "The services and the visualization of the dashboard, right from mapping to end stack of storing is appreciated. Support is also the key factor."
On the more critical side, users said some features / options are nested within each other, so in order to perform certain operations, you have to follow a series of steps over and over again. One user said, "Even on the fastest servers, it slows down a little and the components individually can offer more."
Pros
Automated metadata harvesting with machine learning to reduce manual work
Supports all data domains, not just cloud or on-site storage
Allows for hybrid, multi-cloud support with modern data architectures
Enables socialized data governance through metadata collaboration
Provides end-to-end data lineage tracing from any external source
Cons
Optimized for Talend’s product suite and lacks some open-source capabilities when compared to other data catalog solutions
Limited capabilities outside of core metadata management
Potential dependency and vendor lock-in to Talend's cloud data ecosystem
Machine learning metadata enrichment is still in its beginning stages
Potential loss of metadata from the source systems during the import process
Users need to contact sales to get specifics on pricing. However, Talend offers four different solutions depending on the capabilities and personas using the tool:
Stitch: No-code ingestion for busy analysts
Data Management Platform: A starting point for data professionals and data teams
Big Data Platform: Advanced analytics for cross-team initiatives
Data Fabric: The most complete data integration and sharing solution across the enterprise
Business users care about one thing: finding the information they need in a usable, understandable, contextualized form. Data catalog tools provide a searchable inventory of all your organization's data assets, reducing the time and effort needed to locate relevant data and empowering data consumers to discover actionable knowledge and insights.
Your data catalog tool stores metadata about your data assets, such as data sources, formats, and lineage. In most catalogs, users can rate data and provide comments and feedback to document tribal knowledge about data assets. Metadata enriches and contextualizes your data, enabling data professionals to see where it’s from, how it can be used, and even surface other relevant and related data assets. In short, data catalog tools help translate data into your business’s language, making it more intelligible, reliable, and usable.
Data catalogs provide a central location to manage data governance policies and standards for data security, privacy, and stewardship and help ensure compliance with regulations. They also offer the functionality to onboard users, audit usage, control access, facilitate debugging, track end-to-end lineage, and ensure data accuracy and completeness.
Data catalog tools provide visibility into your data’s lifecycle, helping verify the data's accuracy and reliability. The ability to trace data’s lineage back to its source reduces errors and builds accountability and trust in data handling processes. When data used in analyses is both current and accurate, data teams gain confidence in their data products and the analyses and decisions those products support.
What core use cases do you want your data catalog to solve for? Who will access your data catalog? What’s your budget?
There are many data catalog partners to choose from, and no single solution can solve all your data problems. Your use cases, personas, and budget should inform your criteria for evaluating data catalogs.
Here are seven categories of criteria to consider:
Discovery: How well does the data catalog ingest, enrich, and organize your metadata to make it easy and quick to find and access your data?
Curation: Does the solution present information in easy-to-understand ways?
User Experience: Is the user experience intuitive and approachable, helping both technical users and business consumers use data smoothly in their everyday tasks?
Integration: Is it easy to install and configure? Does it effectively integrate diverse data sources? Does it integrate with third-party tools in your data analytics workflow and allow you to build your own custom integrations if needed?
Governance: Is it easy to add users, control access, and track data lineage and user behavior?
Collaboration: Does it have collaboration features that allow experts from different domains to work together to build context for metadata, creating a single source of truth?
Architecture: Can the platform securely scale as your business demands and data pipelines grow?
Check out our deep dive on data catalogs to learn more about how to evaluate and interpret the capabilities and different approaches of data catalog providers.
Data.world is the only data catalog built on a knowledge graph architecture, and our data queries are 3x more accurate than traditional data catalogs.
To learn more about why the leading data teams choose data.world, schedule a demo today.
