Best Machine Learning Data Catalog Software

How Many Machine Learning Data Catalog Software Products Does G2 Track?

Total Products under this Category: 88

Category Stats (Sep 2026)

  • Average Rating: 4.37/5 (↑0.01 vs Aug 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Cloudera (+2.13%) - Among all products in this category, Cloudera recorded the largest rating increase compared to last month

Last updated: September 15, 2026

How Does G2 Rank Machine Learning Data Catalog Software Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 2,100+ Authentic Reviews
  • 88+ Products
  • Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

G2 Grid® for Machine Learning Data Catalog Software

G2 Grid® for Machine Learning Data Catalog Software plotting products by satisfaction and market presence

Highlighted products: Atlan, AWS Glue, Alation, erwin Data Modeler, Google Cloud Data Catalog, Appen, Cloudera, and Informatica Data & AI Governance, Privacy.

Underlying data: [Grid® JSON](https://www.g2.com/categories/machine-learning-data-catalog/grids.json?focus%5B%5D=atlan&focus%5B%5D=aws-glue&focus%5B%5D=alation&focus%5B%5D=quest-software-erwin-data-modeler&focus%5B%5D=google-cloud-data-catalog&focus%5B%5D=appen&focus%5B%5D=cloudera&focus%5B%5D=informatica-data-ai-governance-privacy)

Atlan

Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve.

Average Rating: 4.5/5.0

Total Reviews: 134

How Do G2 Users Rate Atlan?

  • Ease of Use: 9.0/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 9.2/10 (Category avg: 8.5/10)
  • Metadata Management : 9.4/10 (Category avg: 8.4/10)
  • Data Lineage: 9.3/10 (Category avg: 8.7/10)

Who Is the Company Behind Atlan?

  • Seller: Atlan
  • Company Website:
  • Year Founded: 2019
  • HQ Location: New York, US
  • Twitter: @AtlanHQ
    9,804 Twitter followers
  • LinkedIn® Page: in.linkedin.com
    558 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Financial Services, Information Technology and Services
  • Company Size: 51% Medium, 42% Large

What Do G2 Reviewers Say About Atlan?

AI-generated summary from verified user reviews

Pros
  • Users commend Atlan for its ease of use, making data collaboration smooth and accessible for all.
  • Users appreciate Atlan's intuitive user interface and seamless integration, enhancing collaboration and administrative efficiency.
  • Users appreciate Atlan's exceptional data discovery and collaboration features, simplifying data management and ensuring high quality.
  • Users value the lineage feature of Atlan for its ability to seamlessly integrate and reveal data dependencies.
  • Users appreciate the easy setup of Atlan, enhancing productivity and collaboration without technical barriers.
Cons
  • Users find the learning curve steep with Atlan, requiring time to fully leverage its extensive features.
  • Users note limited functionality in Atlan, particularly with AI capabilities and integration options across teams and tools.
  • Users find user interface issues hinder usability, with limited customization and a steep learning curve for business users.
  • Users find the difficult learning curve with Atlan challenging, particularly due to its many features and configurations.
  • Users report integration issues with Teams and non-native databases, requiring more efficient setup and user management.

What Are Recent G2 Reviews of Atlan?

AWS Glue

AWS Glue is a serverless data integration service that makes it easier for analytics users to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and application develop-ment. You can discover and connect to 70+ diverse data sources, manage your data in a centralized data catalog, and visually create, run, and monitor ETL pipelines to load data into your data lakes. You can im-mediately search and query catalogued data using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

Average Rating: 4.3/5.0

Total Reviews: 194

How Do G2 Users Rate AWS Glue?

  • Ease of Use: 8.4/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.9/10 (Category avg: 8.5/10)
  • Metadata Management : 8.6/10 (Category avg: 8.4/10)
  • Data Lineage: 8.7/10 (Category avg: 8.7/10)

Who Is the Company Behind AWS Glue?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 49% Large, 29% Medium

What Do G2 Reviewers Say About AWS Glue?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of AWS Glue, finding it simple for data preparation and ETL operations.
  • Users value the s seamless data integration capabilities of AWS Glue, enhancing efficiency across analytics and application development.
  • Users appreciate the fully managed ETL service of AWS Glue, enjoying seamless integration and ease of use.
  • Users appreciate the versatile functionality of AWS Glue, simplifying data discovery, preparation, and analytics integration.
  • Users appreciate the ease of implementation and effective support of AWS Glue for seamless data integration and error tracing.
Cons
  • Users experience slow performance with AWS Glue, particularly in startup times and complex debugging processes.
  • Users face difficulties in debugging AWS Glue due to unclear error messages and a steep learning curve.
  • Users often face difficult debugging with AWS Glue, as error messages are not always clear and can be frustrating.
  • Users experience performance issues with AWS Glue, such as slow startup times and complex debugging processes.
  • Users note that AWS Glue can be time-consuming due to slow startup times and complex debugging processes.

What Are Recent G2 Reviews of AWS Glue?

What Are G2 Users Discussing About AWS Glue?

Alation

Alation is the creator of AIOS™, the open, governed, self-improving intelligence operating system for enterprises that cannot afford to get AI wrong. Having pioneered the data catalog market, Alation helps organizations establish trust in the agents, data, and context that power AI-driven decisions. The result is AI organizations can trust—enabling them to navigate regulatory complexity, maintain data fidelity, accelerate operational outcomes, and transform AI ambition into trusted business impact. Alation partners with the world’s leading to leverage better data to solve their most critical challenges.

Average Rating: 4.4/5.0

Total Reviews: 90

How Do G2 Users Rate Alation?

  • Ease of Use: 8.3/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.7/10 (Category avg: 8.5/10)
  • Metadata Management : 7.9/10 (Category avg: 8.4/10)
  • Data Lineage: 7.3/10 (Category avg: 8.7/10)

Who Is the Company Behind Alation?

  • Seller: Alation
  • Company Website:
  • Year Founded: 2012
  • HQ Location: Redwood City, CA
  • Twitter: @Alation
    3,567 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    576 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 58% Large, 27% Medium

What Do G2 Reviewers Say About Alation?

AI-generated summary from verified user reviews

Pros
  • Users praise Alation for its ease of use, facilitating data exploration and enhancing overall efficiency in data management.
  • Users value the flexible data discovery features of Alation, enhancing governance and improving data interpretation.
  • Users appreciate the intuitive navigation of Alation, making information retrieval quick and enhancing productivity significantly.
  • Users find Alation's data cataloging features invaluable for efficient data exploration and enhanced trust in data quality.
  • Users appreciate the intuitive user interface of Alation, making navigation and information retrieval remarkably efficient.
Cons
  • Users report slow performance when integrating multiple data sources and loading larger datasets, impacting overall efficiency.
  • Users feel Alation lacks mature AI features and faces issues with data profiling and inconsistent lineage analytics.
  • Users note the limited functionality of Alation, particularly with the restricted features in non-enterprise licenses.
  • Users face significant lineage limitations, including bugs and inconsistencies that hinder effective data management across systems.
  • Users experience user interface issues with Alation, particularly slow navigation and a less intuitive design for newcomers.

What Are Recent G2 Reviews of Alation?

What Are G2 Users Discussing About Alation?

erwin Data Modeler

Part of the Quest erwin Data Management Platform, delivering industry-leading enterprise data modeling. erwin Data Modeler provides the blueprints for trusted data. Integrated with erwin Data Intelligence, it connects models to governed metadata and business context - ensuring that what’s delivered in production matches the design, so data products are accurate, governed, and AI-ready.

Average Rating: 4.2/5.0

Total Reviews: 123

How Do G2 Users Rate erwin Data Modeler?

  • Ease of Use: 8.4/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.3/10 (Category avg: 8.5/10)
  • Metadata Management : 8.3/10 (Category avg: 8.4/10)
  • Data Lineage: 8.5/10 (Category avg: 8.7/10)

Who Is the Company Behind erwin Data Modeler?

  • Seller: Quest Software
  • Company Website:
  • Year Founded: 1987
  • HQ Location: Austin, TX
  • Twitter: @Quest
    17,109 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    3,507 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 42% Small, 37% Medium

What Do G2 Reviewers Say About erwin Data Modeler?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of erwin Data Modeler, facilitating collaboration and clarity in data visualization.
  • Users value the ease of collaboration in erwin Data Modeler, enhancing alignment between technical and business teams.
  • Users appreciate the clarity in designing and visualizing database structures with erwin Data Modeler, enhancing collaboration and reducing errors.
  • Users value the ease of use for data governance in erwin Data Modeler, facilitating better collaboration and reporting.
  • Users value the seamless data management capabilities of erwin Data Modeler across various environments and data sources.
Cons
  • Users find the licensing costs high, prompting many to switch to more affordable data modeling solutions.
  • Users find the complexity of erwin Data Modeler challenging, especially for new users navigating its outdated interface.
  • Users find the difficult interface of erwin Data Modeler challenging, especially for newcomers to data modeling.
  • Users find the limited customization options of erwin Data Modeler restrict their experience and flexibility in design.
  • Users find the outdated design of erwin Data Modeler makes it challenging for new users to adapt quickly.

What Are Recent G2 Reviews of erwin Data Modeler?

What Are G2 Users Discussing About erwin Data Modeler?

Google Cloud Data Catalog

A fully managed and highly scalable data discovery and metadata management service.

Average Rating: 4.4/5.0

Total Reviews: 25

How Do G2 Users Rate Google Cloud Data Catalog?

  • Ease of Use: 8.7/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.5/10 (Category avg: 8.5/10)
  • Metadata Management : 9.1/10 (Category avg: 8.4/10)
  • Data Lineage: 7.8/10 (Category avg: 8.7/10)

Who Is the Company Behind Google Cloud Data Catalog?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • Twitter: @google
    31,899,995 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 46% Small, 29% Medium

What Are Recent G2 Reviews of Google Cloud Data Catalog?

Appen

Appen collects and labels images, text, speech, audio, video, and other data to create training data used to build and continuously improve the world’s most innovative artificial intelligence systems. We offer a state of the art, licensable data annotation platform to annotate training data use cases in computer vision and natural language processing. Our platform enhances accuracy and efficiency through our Smart Labeling and Pre-Labeling features which use Machine Learning to ease human annotations. You choose the level of service and security you want for data collection and annotation, from white-glove managed service to flexible self-service. Our expertise includes having a global crowd of over 1 million skilled contractors who speak over 235 languages and dialects, in over 70,000 locations and 170 countries, and the industry’s most advanced AI-assisted data annotation platform. Our reliable training data gives leaders in technology, automotive, financial services, retail, healthcare, and governments the confidence to deploy world-class AI products. Founded in 1996, Appen has customers and offices globally.

Average Rating: 4.2/5.0

Total Reviews: 33

How Do G2 Users Rate Appen?

  • Ease of Use: 8.2/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.2/10 (Category avg: 8.5/10)
  • Metadata Management : 8.0/10 (Category avg: 8.4/10)
  • Data Lineage: 7.8/10 (Category avg: 8.7/10)

Who Is the Company Behind Appen?

  • Seller: Appen
  • Year Founded: 1996
  • HQ Location: Kirkland, Washington, United States
  • LinkedIn® Page: www.linkedin.com
    21,182 employees on LinkedIn®
  • Ownership: ASX:APX
  • Total Revenue (USD mm): $244,900

Who Uses This Product?

  • Top Industries: Information Technology and Services
  • Company Size: 54% Small, 26% Medium

What Do G2 Reviewers Say About Appen?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the flexibility and engaging diversity of tasks on Appen, enhancing their work experience and enjoyment.
  • Users appreciate the ease of use of Appen, allowing task completion conveniently via their cell phones.
  • Users appreciate the flexibility of Appen, allowing them to engage in diverse and interesting projects.
Cons
  • Users experience frequent work interruptions due to inconsistent project availability and navigation issues, affecting income reliability.
  • Users find the low compensation and inconsistent work availability makes Appen unreliable for a steady income.
  • Users find the navigation confusing and experience frequent logouts, impacting their usability of Appen.
  • Users often experience connectivity issues with Appen, leading to confusion and frequent disconnections.
  • Users find the navigation confusing and report frequent disconnections, impacting their overall experience with Appen.

What Are Recent G2 Reviews of Appen?

Cloudera

Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives. Cloudera Anywhere Cloud™: Build and scale applications across any environment. The modular hybrid data and AI platform engineered for the agentic era empowers teams to deploy production-grade data and AI workloads across multi-cloud, on-premises, and sovereign environments while maintaining digital sovereignty. The Cloudera data and AI platform includes: Cloudera AI: Deploy and scale any AI model, anywhere. Cloudera brings compute to governed data where it lives for Private AI anywhere by design. Complete control, security, and governance of mission-critical data, models, agents, and inference ensure faster sovereign AI deployments. Cloudera Data-in-Motion: Make fast decisions from real-time data anywhere. Move data with any structure from any source to any destination seamlessly across hybrid environments, enabling in-the-moment business-critical decisions by processing and analyzing real-time data anywhere, from the edge to AI, as business happens. Cloudera Open Data Lakehouse: Process any data, anywhere, for actionable insights. Make smart decisions with an open data lakehouse powered by Apache Iceberg that delivers trusted, reliable, and unified data to fuel agents, AI applications, and analytics, improving collaboration, breaking silos, and simplifying sharing. Cloudera Unified Data Fabric: Unify security and governance across the entire data estate. Move beyond fragmented data management: Break down silos and connect disparate data sources intelligently and securely to provide a unified view of all organizational data and centralized end-to-end control across complex hybrid data environments.

Average Rating: 4.2/5.0

Total Reviews: 190

How Do G2 Users Rate Cloudera?

  • Ease of Use: 8.3/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.9/10 (Category avg: 8.5/10)
  • Metadata Management : 9.1/10 (Category avg: 8.4/10)
  • Data Lineage: 8.8/10 (Category avg: 8.7/10)

Who Is the Company Behind Cloudera?

  • Seller: Cloudera
  • Company Website:
  • Year Founded: 2008
  • HQ Location: Santa Clara, CA
  • Twitter: @cloudera
    106,442 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    3,505 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Banking
  • Company Size: 39% Large, 35% Small

What Do G2 Reviewers Say About Cloudera?

AI-generated summary from verified user reviews

Pros
  • Users praise the user-friendly interface of Cloudera, highlighting its simplicity in managing big data efficiently.
  • Users value the easy scalability of Cloudera, enabling efficient management of large amounts of data effortlessly.
  • Users value the robust security features of Cloudera, ensuring safe and reliable data management across platforms.
  • Users value the comprehensive suite of tools in Cloudera for effective data management and analytics.
  • Users find Cloudera's scalability and centralized administration invaluable for efficient monitoring and management of data processes.
Cons
  • Users express concerns over the high costs of Cloudera, noting it's expensive for its complexity and maintenance.
  • Users find Cloudera's database to be complex, making it challenging for inexperienced professionals to utilize effectively.
  • Users find Cloudera's setup difficult to learn, particularly challenging for beginners without adequate tutorials or guidance.
  • Users find the poor documentation of Cloudera frustrating, complicating navigation and setup for complex data configurations.
  • Users often face access issues with Cloudera, particularly with unauthorized errors in Airflow tasks and limited documentation.

What Are Recent G2 Reviews of Cloudera?

What Are G2 Users Discussing About Cloudera?

Informatica Data & AI Governance, Privacy

Informatica Data & AI Governance, Privacy is a comprehensive, cloud-native solution designed to empower organizations with predictive data intelligence. By integrating data discovery, cataloging, governance, and lineage capabilities, it enables businesses to find, understand, trust, and access their data assets efficiently. This unified approach simplifies collaboration between technical and business teams, ensuring that data-driven decisions are based on accurate and trustworthy information. With AI-powered automation, the platform enhances data classification, curation, and quality management, facilitating faster and more reliable analytic insights. By providing a holistic view of data relationships and lineage, Informatica Cloud Data Governance and Catalog helps organizations turn their data into a competitive advantage. Key Features and Functionality: - Automated Data Discovery and Classification: Utilizes AI to automatically find, classify, and inventory critical data across cloud and on-premises environments. - Comprehensive Data Cataloging: Creates a centralized repository of data assets, linking technical metadata with business context for enhanced understanding. - End-to-End Data Lineage: Provides visual representations of data flow and transformations, enabling users to trace data origins and assess impact. - Integrated Data Quality Management: Monitors and ensures data quality through profiling, validation, and cleansing processes. - Collaboration and Social Curation: Facilitates teamwork by allowing users to share insights, certify data assets, and engage in discussions through comments and ratings. - AI Model Governance: Manages and governs AI models alongside data, ensuring compliance and trust in AI-driven decisions. Primary Value and Problem Solved: Informatica Data & AI Governance, Privacy addresses the critical need for organizations to manage and govern their data assets effectively in an increasingly complex data landscape. By providing a unified platform that automates data discovery, classification, and quality management, it ensures that businesses can trust their data for decision-making. The solution enhances collaboration between technical and business users, linking technical metadata with business context to provide a holistic view of data assets. This comprehensive approach not only accelerates the delivery of reliable analytic insights but also ensures compliance with data governance policies, ultimately turning data into a strategic asset that drives innovation and competitive advantage.

Average Rating: 4.1/5.0

Total Reviews: 144

How Do G2 Users Rate Informatica Data & AI Governance, Privacy?

  • Ease of Use: 8.1/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.1/10 (Category avg: 8.5/10)
  • Metadata Management : 8.1/10 (Category avg: 8.4/10)
  • Data Lineage: 8.6/10 (Category avg: 8.7/10)

Who Is the Company Behind Informatica Data & AI Governance, Privacy?

  • Seller: Informatica
  • Company Website:
  • Year Founded: 1993
  • HQ Location: Redwood City, CA
  • Twitter: @Informatica
    99,643 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    2,473 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 45% Large, 30% Small

What Do G2 Reviewers Say About Informatica Data & AI Governance, Privacy?

AI-generated summary from verified user reviews

Pros
  • Users value the automatic discovery and governance of enterprise data, enhancing clarity and trust in data management.
Cons
  • Users often face integration issues due to complex setup and design, especially in large enterprises with legacy systems.

What Are Recent G2 Reviews of Informatica Data & AI Governance, Privacy?

What Are G2 Users Discussing About Informatica Data & AI Governance, Privacy?

Collibra

Try Collibra for free @ Collibra.com/tour Collibra is for organizations with complex data challenges, hybrid data ecosystems—and big ambitions for data and AI. We help organizations who are trying to accelerate data and AI use cases while ensuring compliance, but are struggling with fragmented governance and visibility across the whole hybrid data ecosystem. Collibra unifies governance for data and AI across every system, data source and user—to create safe autonomy and a foundation for scaling AI and data use cases. With Collibra, you can accelerate all your data and AI use cases, safely and with well–understood data. That’s Data Confidence.

Average Rating: 4.2/5.0

Total Reviews: 99

How Do G2 Users Rate Collibra?

  • Ease of Use: 8.0/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.3/10 (Category avg: 8.5/10)
  • Metadata Management : 8.0/10 (Category avg: 8.4/10)
  • Data Lineage: 8.0/10 (Category avg: 8.7/10)

Who Is the Company Behind Collibra?

  • Seller: Collibra
  • Company Website:
  • Year Founded: 2008
  • HQ Location: New York, New York
  • Twitter: @collibra
    5,756 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1,095 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Financial Services, Banking
  • Company Size: 72% Large, 18% Medium

What Do G2 Reviewers Say About Collibra?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the unified data intelligence platform of Collibra, effectively aligning Business and IT on a single system.
  • Users value the strong data governance features of Collibra, enhancing data accessibility and trustworthiness across the organization.
  • Users value the effective data management capabilities of Collibra, enhancing compliance, governance, and integration for optimal use.
  • Users appreciate the collaborative tools of Collibra, enhancing communication and efficiency in managing complex data ecosystems.
  • Users value Collibra for its reliable and user-friendly data intelligence platform, enhancing productivity through seamless integration and support.
Cons
  • Users note the complexity issues in Collibra, particularly with setup, bugs, and inconsistent API stability.
  • Users find Collibra's complexity hinders efficiency, as setup, configuration, and user engagement can be overwhelming.
  • Users struggle with limited functionality due to complex navigation and unintuitive language in Collibra.
  • Users find the difficult setup of Collibra to require significant time and resources, impacting their overall experience.
  • Users find Collibra to be expensive, with high licensing fees and challenges in adoption impacting user experience.

What Are Recent G2 Reviews of Collibra?

What Are G2 Users Discussing About Collibra?

decube

Decube is a Context Layer platform specifically designed for the AI era, providing organizations with the ability to give their data meaning, memory, and trust. This innovative system integrates various components such as metadata management, automated lineage tracking, data quality assurance, and observability to create a comprehensive real-time map of data dynamics. By understanding how data operates, flows, and its reliability, Decube empowers enterprises to make informed decisions and effectively manage AI workloads. Targeted primarily at enterprises that rely heavily on data-driven decision-making, Decube addresses a critical challenge faced by many organizations: the lack of contextual understanding of their data. In an age where data is abundant, the real issue lies in the ability to interpret and utilize that data effectively. Decube provides a connected understanding of the entire data ecosystem, which helps eliminate blind spots and enhances governance. This contextual awareness is essential for organizations looking to leverage AI technologies and ensure that their models, dashboards, and agents operate with greater intelligence and safety. Key features of Decube include its robust metadata management capabilities, which allow users to track and manage data lineage effortlessly. This feature ensures that organizations can trace the origins and transformations of their data, thereby enhancing transparency and accountability. Additionally, Decube’s focus on data quality means that users can trust the information they are working with, reducing the risk of errors in critical decision-making processes. The observability aspect of the platform further enables organizations to monitor data flows in real-time, ensuring that any issues can be identified and addressed promptly. The benefits of using Decube extend beyond mere data management. By providing a living, interconnected understanding of data, Decube enhances the overall operational confidence of organizations. This platform not only strengthens governance but also facilitates smarter decision-making by ensuring that all data-driven models are built on a foundation of reliable and contextualized information. As businesses increasingly depend on trustworthy data and AI-ready infrastructure, Decube stands out as a vital tool that equips them with the necessary context to navigate the complexities of the modern data landscape.

Average Rating: 4.6/5.0

Total Reviews: 23

How Do G2 Users Rate decube?

  • Ease of Use: 9.4/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 9.6/10 (Category avg: 8.5/10)
  • Metadata Management : 9.6/10 (Category avg: 8.4/10)
  • Data Lineage: 9.6/10 (Category avg: 8.7/10)

Who Is the Company Behind decube?

  • Seller: Decube Data
  • Company Website:
  • Year Founded: 2022
  • HQ Location: Kuala Lumpur
  • Twitter: @decube_data
    113 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    46 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services
  • Company Size: 39% Medium, 35% Small

What Do G2 Reviewers Say About decube?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the intuitive and efficient UI of Decube, simplifying data monitoring and issue detection.
  • Users appreciate the ease of use of Decube, enjoying its user-friendly interface and seamless data monitoring.
  • Users love the visibility into data pipelines provided by Decube, enabling early detection of issues and reliable data management.
  • Users value Decube for its robust data quality features, ensuring early detection of issues and reliable data management.
  • Users value Decube for its clear insights and easy data monitoring, making it simple to manage data pipelines effectively.
Cons
  • Users find the limited functionality of decube frustrating, as deeper insights require extra effort and manual configuration.
  • Users find the complex setup of Decube time-consuming and occasionally overwhelming during initial configuration.
  • Users find the limited features of Decube require extra effort for deeper insights and configurations.
  • Users find the missing features in Decube, such as API monitoring and group-by options, frustrating and limiting.
  • Users frequently experience poor customer support, often finding it difficult to reach assistance when needed.

What Are Recent G2 Reviews of decube?

Select Star

Select Star is a modern data governance platform that helps organizations manage and understand their data at scale, enabling AI, analytics, and self-service across the business. It automatically catalogs datasets, traces end-to-end lineage, and builds a shared business glossary and semantic layer, so teams can confidently work with trusted data. With a user-friendly data portal and built-in automation, Select Star supports use cases including data democratization, data governance, semantic layers, and cloud data migrations serving as a foundational layer for enterprise AI and data initiatives.

Average Rating: 4.5/5.0

Total Reviews: 55

How Do G2 Users Rate Select Star?

  • Ease of Use: 8.9/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 8.2/10 (Category avg: 8.5/10)
  • Metadata Management : 8.7/10 (Category avg: 8.4/10)
  • Data Lineage: 8.9/10 (Category avg: 8.7/10)

Who Is the Company Behind Select Star?

  • Seller: Select Star
  • Year Founded: 2020
  • HQ Location: San Francisco, CA
  • Twitter: @selectstarhq
    389 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    16 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Real Estate
  • Company Size: 51% Medium, 38% Large

What Do G2 Reviewers Say About Select Star?

AI-generated summary from verified user reviews

Pros
  • Users praise the ease of use of Select Star, allowing effortless integration and efficient data management.
  • Users value the insightful data lineage offered by Select Star, enhancing understanding and management of data effectively.
  • Users value the clean and intuitive UI of Select Star, enhancing data discovery and simplifying complex tasks.
  • Users value the easy data discovery features of Select Star, significantly simplifying navigation in complex data models.
  • Users value the ease of data discovery in Select Star, enhancing their ability to locate and analyze metadata efficiently.
Cons
  • Users find limited functionality in Select Star, wishing for broader insights and enhanced reporting capabilities.
  • Users face lineage limitations with Select Star, struggling to visualize dependencies and access permissions in complex queries.
  • Users note a complex setup for Select Star, especially in configuring a unified project for dbt mesh ecosystems.
  • Users find difficult learning curves with Select Star, requiring clearer guidance for quicker value extraction.
  • Users find the expertise required for Select Star to be a barrier, seeking more accessible guidance for faster value.

What Are Recent G2 Reviews of Select Star?

IBM InfoSphere Information Governance Catalog

IBM® Information Governance Catalog is an interactive, web-based tool that allows users to explore, understand and analyze information. Users can create, manage and share a common business language, document and enact policies and rules and track the usage and consumption of data within a lineage report providing trusted information for compliance and insights. Learn More: https://ibm.co/2xmfLsK

Average Rating: 4.0/5.0

Total Reviews: 16

How Do G2 Users Rate IBM InfoSphere Information Governance Catalog?

  • Ease of Use: 7.6/10 (Category avg: 8.6/10)

Who Is the Company Behind IBM InfoSphere Information Governance Catalog?

  • Seller: IBM
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • Twitter: @IBMSecurity
    74,660 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Company Size: 53% Large, 26% Medium

What Are Recent G2 Reviews of IBM InfoSphere Information Governance Catalog?

Secoda

Secoda is an AI-powered data governance platform designed to help organizations explore, understand, and utilize their data effectively. By providing a comprehensive platform that connects to 75+ data sources, pipelines, warehouses, and visualization tools, Secoda aims to create a unified source of truth for businesses. This functionality is particularly valuable for organizations looking to enhance their self-serve analytics, streamline operations, and improve decision-making. Targeted at data teams, business stakeholders, and organizations of all sizes, Secoda serves as an essential tool for those who need to manage and interpret large volumes of data. Its user-friendly interface ensures that individuals with varying levels of technical expertise can leverage the platform to gain actionable insights. Companies such as Vanta, Cardinal Health, ID.me, and Dialpad have adopted Secoda to monitor the health of their data ecosystems, enhance the efficiency of their data teams, and scale AI readiness. One of Secoda’s core advantages is its ability to unify data cataloging, enterprise governance, and observability into a single, streamlined platform. This consolidation not only reduces the overhead of managing multiple tools but also powers Secoda AI with rich, connected context, enabling teams to focus on insights instead of infrastructure. Secoda automates key data management tasks including documentation, tagging, glossary term creation, and policy creation. This automation enables users to quickly discover and access relevant data and insights without extensive manual effort. By streamlining these processes, Secoda not only saves valuable time but also empowers teams to make confident, data-driven decisions based on current, well-organized information, ultimately driving better business outcomes. Overall, Secoda stands out in the data management landscape by offering a comprehensive, AI-driven solution that caters to the needs of both technical and non-technical users. Its ability to create a single source of truth, coupled with its integration of multiple functionalities into one platform, positions it as a valuable asset for organizations aiming to harness the full potential of their data.

Average Rating: 4.5/5.0

Total Reviews: 55

How Do G2 Users Rate Secoda?

  • Ease of Use: 8.2/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 9.3/10 (Category avg: 8.5/10)
  • Metadata Management : 9.5/10 (Category avg: 8.4/10)
  • Data Lineage: 8.9/10 (Category avg: 8.7/10)

Who Is the Company Behind Secoda?

  • Seller: Secoda
  • Year Founded: 2021
  • HQ Location: Toronto, CA
  • Twitter: @SecodaHQ
    923 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    19 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Financial Services
  • Company Size: 65% Medium, 18% Small

What Do G2 Reviewers Say About Secoda?

AI-generated summary from verified user reviews

Pros
  • Users value the data lineage feature of Secoda for enhancing metadata integration and improving asset discovery across teams.
  • Users praise Secoda's easy onboarding and self-serve nature, enhancing data governance and user empowerment from day one.
  • Users value the integration capabilities of Secoda, seamlessly connecting with numerous platforms for enhanced data management.
  • Users appreciate the ease of learning with Secoda, noting its intuitive design and supportive resources for all users.
  • Users praise the helpful data lineage and questions features of Secoda for enhancing knowledge sharing and understanding.
Cons
  • Users find the learning difficulty with integrations and team systems to be a frustrating barrier to effective usage.
  • Users note that Secoda has product immaturity, with occasional bugs and a lack of some essential features.
  • Users express concerns over improvement needed for filtering assets, AI model descriptions, and overall user experience in Secoda.
  • Users find Secoda has limited functionality with an overwhelming interface and insufficient support for essential features.
  • Users report performance issues in Secoda, including syncing delays and frequent bugs affecting functionality and user experience.

What Are Recent G2 Reviews of Secoda?

IBM watsonx.data intelligence

IBM watsonx.data intelligence revolutionizes the way organizations curate, manage, and utilize data by leveraging the power of AI to simplify data delivery across hybrid ecosystems. IBM watsonx.data intelligence is a comprehensive solution that integrates capabilities such as data governance (formerly IBM Knowledge Catalog), data lineage (formerly IBM Manta Data Lineage), data sharing, and data quality management. It empowers organizations to discover, trust, and access meaningful data, providing consumers with reliable data products. Explore Demo Library - https://www.ibm.com/products/watsonx-data-intelligence/demo-library Start your free trial - https://dataplatform.cloud.ibm.com/registration/stepone?context=df&apps=all&uucid=1227cc9e37cb9292&preselect_region=true

Average Rating: 4.2/5.0

Total Reviews: 24

How Do G2 Users Rate IBM watsonx.data intelligence?

  • Ease of Use: 8.4/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 7.5/10 (Category avg: 8.5/10)
  • Metadata Management : 7.5/10 (Category avg: 8.4/10)
  • Data Lineage: 8.3/10 (Category avg: 8.7/10)

Who Is the Company Behind IBM watsonx.data intelligence?

  • Seller: IBM
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • Twitter: @IBMSecurity
    74,660 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Company Size: 38% Small, 34% Large

What Do G2 Reviewers Say About IBM watsonx.data intelligence?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the automation features of IBM watsonx.data intelligence, enhancing understanding of data flow and mitigating risks.
  • Users appreciate the easy visualization of data flows with IBM Manta, enhancing data governance and insights.
  • Users value the easy visualization of data flows with IBM Watsonx.data intelligence, enhancing data governance and insight generation.
  • Users value the ease of use of IBM Watsonx.data intelligence, facilitating seamless data understanding and governance.
  • Users appreciate the efficiency of IBM watsonx.data intelligence, streamlining data processes and enhancing decision-making capabilities.
Cons
  • Users find the complex implementation of IBM watsonx.data intelligence challenging, often needing specialized expertise for setup.
  • Users find the setup complexity of IBM watsonx.data intelligence requires specialized expertise, making implementation challenging.
  • Users find IBM watsonx.data intelligence expensive, with high implementation costs and ongoing financial commitments.
  • Users note that specialized expertise is required for proper implementation, adding to the complexity of setup.
  • Users find the extra costs of IBM watsonx.data intelligence can be significant, making it less accessible for some.

What Are Recent G2 Reviews of IBM watsonx.data intelligence?

Coalesce Catalog (formerly CastorDoc)

Coalesce Catalog is a collaborative, automated data discovery & catalog tool. We believe that data people spend way too much time trying to find and understand their data. Coalesce Catalog redesigns how data people collaborate. It provides a single source of truth to reference and document all the knowledge related to data within your company. If you are looking for a table related to your customers, just look for it as you would in Google, and Coalesce Catalog provides you with all the context you will need for your analysis. Inspired by internal tools developed by Uber, Airbnb, Lyft, and Spotify, Coalesce Catalog has developed a plug-and-play solution that deploys in minutes to drive value for companies of all sizes. Discover and catalog your data today with Coalesce Catalog.

Average Rating: 4.7/5.0

Total Reviews: 63

How Do G2 Users Rate Coalesce Catalog (formerly CastorDoc)?

  • Ease of Use: 9.6/10 (Category avg: 8.6/10)
  • Business and Data Glossary: 9.9/10 (Category avg: 8.5/10)
  • Metadata Management : 9.9/10 (Category avg: 8.4/10)
  • Data Lineage: 9.9/10 (Category avg: 8.7/10)

Who Is the Company Behind Coalesce Catalog (formerly CastorDoc)?

  • Seller: Coalesce
  • Company Website:
  • Year Founded: 2020
  • HQ Location: San Francisco, CA
  • LinkedIn® Page: www.linkedin.com
    104 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 59% Medium, 27% Large

What Do G2 Reviewers Say About Coalesce Catalog (formerly CastorDoc)?

AI-generated summary from verified user reviews

Pros
  • Users find Coalesce Catalog's ease of use exceptional, simplifying data discovery and enhancing collaboration across teams.
  • Users appreciate the collaboration features of Coalesce Catalog, enhancing knowledge sharing and onboarding processes.
  • Users appreciate the seamless connectivity of Coalesce Catalog, simplifying data discovery and integration across various sources.
  • Users find the data lineage feature invaluable for simplifying data discovery and enhancing team collaboration.
  • Users find the lineage feature extremely useful for simplifying data discovery and enhancing connectivity across data sources.
Cons
  • Users face connector issues that prevent direct integration of other AI agents with the Coalesce Catalog knowledge.
  • Users face integration issues, as connecting other AI agents to the Catalog knowledge is not possible.
  • Users face a limitation in AI integration as connecting other agents directly to the Catalog knowledge is not possible.

What Are Recent G2 Reviews of Coalesce Catalog (formerly CastorDoc)?

Shalaka Joshi
SJ
Researched and written by Shalaka Joshi
Updated October 3, 2024

Machine Learning Data Catalog Topics

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Learn More About Machine Learning Data Catalog Software

What is a Machine Learning Data Catalog?

Machine learning data catalog (MLDC) is an automated data catalog that carries out tasks like crawling metadata, cataloging, and classifying personally identifiable information (PII) data. Machine learning data catalogs organize the dataset inventory using metadata.

Data catalogs help companies know where the data is stored, thus reducing the time taken to identify data and making it easily accessible for analytics. They are inventories of assets like tables, schema, files, and charts in organizations, aiding in solving a company's data discovery, quality, and governance challenges.

What does MLDC Stand For?

MLDC is an acronym for Machine Learning Data Catalog. 

What are the Common Features of Machine Learning Data Catalogs?

Machine learning data catalogs simplify the manual functions of a data catalog. A data catalog is an essential part of the data management strategy of any organization. Some of the features of machine learning data catalogs are:

Data ingestion and discovery: Machine learning data catalogs must have prebuilt adapters to connect to different company systems like applications, databases, files, and external APIs. These adapters help in discovering metadata from systems. Metadata can be table names, attribute names, and constraints. The feature helps build native connectivity like integrations for data sources, business intelligence (BI) solutions, and data science tools.

Business glossary: Although a good amount of data is stored in the repository, it is also essential for the users to understand what the stored data means. The glossary feature links this data to business terms giving it more meaning. 

Automated data labeling: Data labeling is a prerequisite for machine learning algorithms. Automated data labeling is more accurate than manual since it eliminates human errors. Data labeling usually involves annotators identifying objects in images to build quality artificial intelligence (AI) training data. Automated labeling eliminates the challenges posed by the tedious annotation cycles.

Data lineage: Data lineage is the process that helps the users know who, why, when, and where changes are made to the data. It is a part of metadata management. MLDCs automate the data lineage process. Data lineage helps determine when new or changed data require retraining machine learning models. MLDCs usually parse through query logs into data lakes and other data sources automatically to create a data lineage map.

Data quality monitoring and anomaly detection: Data quality monitoring helps users understand if the data came from a trusted source. The machine learning data catalog also has a feature to identify sudden changes in data using machine learning algorithms. The users are immediately alerted to any changes or anomalies that are detected. 

Semantic search for data sets: Machine learning data catalogs provide users with visual and intuitive searches like search engines. Almost every user in any organization is a data user, but not everyone can use SQL queries to use data. The semantic search feature makes it easier for all users to discover data sets.

Compliance capabilities: This feature ensures that sensitive data is not exposed and that the user can trust the data. It further helps keep data governance policies in place and strengthen data management in the organization. Data stewards can identify low-quality data and restrict access to sensitive data, thus helping comply with regulations such as the General Data Protection Regulation (GDPR).

Data profiling: Data profiling helps check the data from the data source and collects information about it. This process helps in knowing data quality issues much better, thus making the data management process more efficient.

What are the Benefits of Machine Learning Data Catalogs?

A machine learning data catalog provides several benefits to different types of users in the organization. These include:

Ease in data curation: Data curation is a process of collecting, organizing, labeling, and cleaning data. Machine learning data catalogs validate metadata and organize insights into correct repositories using machine learning algorithms.

Ease of search: Because of semantic search, it becomes easier for non-technical users to search and discover data for use since they do not have to use SQL queries every time to access data.

Ease in data collaboration: Machine learning data catalogs help the users collaborate, use, and share data sets because machine learning data catalogs ease finding and storing siloed data.

Who Uses Machine Learning Data Catalogs?

Machine learning data catalogs centralize metadata for various data assets. By organizing the metadata, MLDCs help organizations to govern data access.

Data analysts: Data analysts use MLDC to discover, classify, and manipulate data for their analytics processes. They can also discover AI or machine learning models, understand how they work, and import them into their BI tools. Data catalogs help data analysts make companies into self-service organizations. Self-service analytics is important for any organization that wants to be driven by insights. Machine learning data catalogs help the users know the means to find, understand, and trust data.

Marketers: Marketing teams use the machine learning data catalog more commercially. They obtain insights for making better decisions using data catalogs.

Data scientists: Data scientists usually publish their models for reuse. Data scientists always look for one platform that centralizes data for different projects. 

Challenges with Machine Learning Data Catalogs

Although machine learning data catalogs help solve major challenges in traditional data catalogs like data discovery and data lineage, MLDCs also come with challenges.  

Scalability: It is tricky for all MLDCs to support a huge metadata volume. Sometimes, the data catalogs break down due to performance issues when overloaded with enormous amounts of metadata. Initially, data used to be stored in the company's mainframe data center. However, due to today's big data, machine learning data catalogs must keep track of data in both cloud and data lakes.

Fragmentation in evaluating a product: If a data catalog is too bulky, it causes fragmentation in the user's journey of evaluating a product. Too much data makes users use too many tools, thus breaking a seamless experience into fragments.

How to Buy Machine Learning Data Catalogs

Requirements Gathering (RFI/RFP) for Machine Learning Data Catalogs

The machine learning data catalog offers many features to help users identify usable data. A buyer can choose the right MLDC software depending on the organization's needs. RFP/RFIs help the organization look for pricing, product features, and guidelines.

Compare Machine Learning Data Catalog Products

Create a long list

The first step is to look for all the possible players in the space. This gives an advantage of evaluating the vendors for the price, product features, and customer service. 

Create a short list

After evaluating the potential vendors, the company can narrow the list to those who check all their boxes.

Conduct demos

Demos help in understanding the product as a whole. A team of IT professionals and data scientists should join these demos to understand the product's functionality, whereas the marketing team can join in to analyze the business use of the software in the projects.

Selection of Machine Learning Data Catalogs

Choose a selection team

A team of marketing professionals with data scientists and IT professionals can communicate any queries related to the MLDC product with the vendors. A data scientist would be more interested in knowing the technical features of the software. A marketing manager would be curious to know how the marketing team could use MLDC for any project. An IT professional would want to understand the software installation procedure.

Negotiation

Once the vendor quotes the price, the negotiations begin. The price is fixed based on the cost of other similar products available in the market and the extent to which the product can solve the challenges.

Final decision

The final decision is based on agreements between the vendor and the buyer.