Best Machine Learning Data Catalog Software

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

Total Products under this Category: 113

Category Stats (Nov 2026)

  • Average Rating: 4.37/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Collate (+2.22%) - Among all products in this category, Collate recorded the largest rating increase compared to last month

Last updated: November 01, 2026

How Does G2 Rank Machine Learning Data Catalog Software Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 2,500+ Authentic Reviews
  • 113+ 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, Alation, AWS Glue, erwin Data Modeler, Google Cloud Data Catalog, Cloudera, Appen, and BMC AMI Data.

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

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: 135

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
  • LinkedIn® Page: in.linkedin.com
    508 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 value the ease of use in Atlan, making data collaboration seamless and enhancing overall productivity.
  • Users appreciate Atlan's intuitive user interface and seamless integration, enhancing collaboration and administrative efficiency.
  • Users value Atlan's easy data discovery and collaboration features, enhancing understanding and trust in their data.
  • Users value the lineage feature of Atlan for its ability to seamlessly integrate and reveal data dependencies.
  • Users love the easy setup of Atlan, facilitating seamless integration and streamlined workflows across teams.
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 prevalent, as navigation is often challenging and lacks customization for line-of-business needs.
  • Users find the difficult learning curve with Atlan challenging, particularly due to its many features and configurations.
  • Users face integration issues with Teams, requiring more refined workflows and additional infrastructure for non-native databases.

What Are Recent G2 Reviews of Atlan?

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: 96

How Do G2 Users Rate Alation?

  • Ease of Use: 8.4/10 (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
  • LinkedIn® Page: www.linkedin.com
    576 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 57% Large, 28% Medium

What Do G2 Reviewers Say About Alation?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Alation, praising its intuitive interface and seamless setup for security and integration.
  • Users commend Alation for its seamless data discovery, facilitating easy navigation and understanding of complex data relationships.
  • Users value the seamless navigation and integration in Alation, enhancing data accessibility and overall user experience.
  • Users highly value the user-friendly data cataloging of Alation, enhancing data discovery and governance across platforms.
  • Users find Alation’s user-friendly interface enhances navigation, simplifying data exploration and project organization effectively.
Cons
  • Users experience slow performance in Alation, particularly with large data integrations and metadata freshness delays.
  • Users note the missing features in Alation, particularly in Data Quality and user interface improvements.
  • Users note limited functionality in Alation, particularly with missing features and restricted data products for non-enterprise licenses.
  • Users face significant lineage limitations with Alation, experiencing bugs and incomplete data connections across systems.
  • Users find the user interface lacking, experiencing slowness and complexity that hinders effective data navigation.

What Are Recent G2 Reviews of Alation?

What Are G2 Users Discussing About Alation?

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?

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 value the ease of use of AWS Glue, appreciating its straightforward integration and user-friendly features.
  • Users benefit from the seamless data integration of AWS Glue, enhancing analytics and application development efficiency.
  • Users appreciate the fully managed ETL service of AWS Glue, simplifying data integration and optimizing costs effectively.
  • Users value the robust functionality of AWS Glue, which simplifies data preparation and enhances analytics capabilities.
  • Users find AWS Glue to have easy implementation and effective error tracing, streamlining their ETL operations significantly.
Cons
  • Users experience slow performance with AWS Glue, including long start-up times and challenging debugging processes.
  • Users find debugging difficult in AWS Glue due to unclear error messages and a steep learning curve.
  • Users find difficult debugging in AWS Glue frustrating, especially due to unclear error messages and slow start-up times.
  • Users experience performance issues with AWS Glue, including slow startup times and complex debugging processes.
  • Users find AWS Glue time-consuming due to slow startup times and complex debugging processes, affecting overall efficiency.

What Are Recent G2 Reviews of AWS Glue?

What Are G2 Users Discussing About AWS Glue?

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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: 131

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
  • LinkedIn® Page: www.linkedin.com
    3,507 employees on LinkedIn®
  • Ownership: NYSE: DGX

Who Uses This Product?

  • Who Uses This: Data Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 43% Small, 35% Medium

What Do G2 Reviewers Say About erwin Data Modeler?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of erwin Data Modeler, streamlining data visualization and collaboration effortlessly.
  • Users value the easy collaboration features of erwin Data Modeler, enhancing alignment between technical and business teams.
  • Users value 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 collaboration and reducing misunderstandings.
  • Users value the robust data management capabilities of erwin Data Modeler, enabling seamless integration with various databases.
Cons
  • Users find the high licensing cost of erwin Data Modeler a significant drawback, limiting wider adoption and usage.
  • Users find the complexity of erwin Data Modeler makes it challenging for new users to adapt efficiently.
  • Users find the difficult interface of erwin Data Modeler challenging, especially for newcomers to data modeling concepts.
  • Users find the limited customization options in erwin Data Modeler hinder their flexibility and personalization of the tool.
  • Users feel that the interface is outdated, making 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
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG
  • Phone: +1 (650) 253-0000

Who Uses This Product?

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

What Are Recent G2 Reviews of Google Cloud Data Catalog?

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: 208

How Do G2 Users Rate Cloudera?

  • 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 : 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
  • LinkedIn® Page: www.linkedin.com
    3,505 employees on LinkedIn®
  • Phone: 888-789-1488

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Banking
  • Company Size: 39% Large, 33% Small

What Do G2 Reviewers Say About Cloudera?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Cloudera, enjoying its straightforward installation and maintenance for analytics.
  • Users commend Cloudera for its exceptional scalability, enabling seamless integration and efficient big data management.
  • Users commend Cloudera for its robust security, ideal for high-scale enterprise-level data engineering tasks.
  • Users appreciate the easy and secure data management provided by Cloudera, enabling smart analytics and decision-making.
  • Users value the user-friendly features of Cloudera, which simplify big data handling and improve workflow efficiency.
Cons
  • Users express concerns over the high costs associated with Cloudera, making it less accessible for some.
  • Users find Cloudera to have a complex setup and operational challenges, especially for inexperienced professionals.
  • Users find Cloudera setup to be difficult to learn, especially for beginners navigating its complex interface.
  • Users find the documentation lacking, which hampers navigating data configurations and resolving errors effectively.
  • Users face significant access issues with Cloudera, including limited documentation and authorization errors during task execution.

What Are Recent G2 Reviews of Cloudera?

What Are G2 Users Discussing About Cloudera?

BMC AMI Data

BMC AMI Data is an intelligent data management and optimization solution for IBM Z environments. It helps enterprises manage, protect, and optimize mission-critical mainframe data while reducing operational complexity, cost, and risk. BMC AMI Data leverages automation, advanced analytics, and predictive insights to streamline data maintenance, improve performance, and ensure the availability of critical workloads across Db2, IMS, VSAM, and related systems. The solution enables data teams to move from reactive management to proactive, insights-driven operations. Key capabilities include: - Automated data management and maintenance to reduce manual effort and improve operational efficiency - Real-time analytics and predictive insights to optimize performance and anticipate issues before they impact workloads - CPU and resource optimization to control costs and improve system efficiency at scale - Data protection and risk reduction to safeguard critical information and maintain data integrity - Support for modernization initiatives by simplifying how mainframe data is managed and integrated with evolving business needs - On-platform processing that keeps data secure and managed directly within the IBM Z environment By modernizing mainframe data management with automation and intelligence, BMC AMI Data helps organizations control data growth, reduce operational risk, and ensure high-performance delivery of always-on, business-critical applications.

Average Rating: 4.4/5.0

Total Reviews: 77

How Do G2 Users Rate BMC AMI Data?

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

Who Is the Company Behind BMC AMI Data?

  • Seller: BMC Software
  • Company Website:
  • Year Founded: 1980
  • HQ Location: Houston, TX
  • LinkedIn® Page: www.linkedin.com
    8,646 employees on LinkedIn®
  • Phone: 713 918 8800

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 46% Large, 32% Small

What Do G2 Reviewers Say About BMC AMI Data?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the analytics capabilities of BMC AMI Data, enhancing efficiency and oversight in database management.
  • Users value the seamless automation in BMC AMI Data, enhancing integration and ensuring quality assurance standards.
  • Users appreciate the ease of use in BMC AMI Data, simplifying database administration tasks effectively.
  • Users appreciate the easy integrations of BMC AMI Data, enhancing workflow with tools like Jenkins and UrbanCode Deploy.
  • Users appreciate the automation and proactive monitoring features of BMC AMI Data, enhancing their database administration efficiency.
Cons
  • Users highlight the expensive pricing of BMC AMI Data, which can be a significant barrier for many.
  • Users highlight installation difficulty with BMC AMI Data, noting challenges that complicate initial setup and administration.
  • Users often face a steep learning curve with BMC AMI Data, making initial use challenging for some.
  • Users find the limited compatibility with older Db2 systems a drawback, hindering seamless integration in workflows.
  • Users note the lack of customization possibilities in BMC AMI Data, limiting tailored solutions for their needs.

What Are Recent G2 Reviews of BMC AMI Data?

What Are G2 Users Discussing About BMC AMI Data?

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 variety of tasks offered by Appen, enhancing their overall work experience.
  • 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 work interruptions due to inconsistent availability and difficulties in project selection, impacting reliability.
  • Users highlight low compensation and inconsistent work availability, making it hard to rely on Appen for 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?

Coalesce

Coalesce is the data operating layer for analytics and AI. It unifies data transformation, cataloging, and quality in one governed platform. Coalesce Transform, Coalesce Catalog, and Coalesce Quality share one metadata layer, one set of definitions, and one governance model. • One system, not a stitched toolchain: Transform, Catalog, and Quality share one metadata layer, one set of definitions, and one governance model. • Govern every actor, human or AI: an agent follows the same standards, approvals, and lineage as an engineer in the IDE. • A purpose-built place to review AI's work: the visual graph, node inspection, and column-level lineage let humans verify agent-built pipelines before they ship, with an audit trail that separates agent from human work. • Request to production in one flow: no handoffs across tools and teams; governed by default. Coalesce Transform: Build, automate, and govern data transformations—with agents, within the UI, or directly through code. Engineers work in the visual builder, the IDE, or locally through Coalesce Desktop and the coa CLI. Every pipeline compiles to optimized SQL with column-level lineage, reusable Node Types and Packages, and validation on every change. Coalesce Catalog: Discover, document, and govern every data asset with AI. Catalog connects across your stack, documents assets with AI, and keeps lineage, ownership, and definitions current automatically—so people and AI agents work from the same understanding of the data. Coalesce Quality: Monitor, test, and validate data with observability built into the pipeline. Tests, rules, and anomaly monitors run as part of the pipeline. When something breaks, lineage-aware alerting shows what's affected, who owns it, and where the failure started. Built for humans and AI agents: Coalesce Copilot turns natural-language intent into governed transformations. Scout, an always-on data SRE, triages issues and proposes fixes for human review. Transform MCP, Catalog MCP, and Quality MCP let AI agents like Claude Code read metadata, traverse lineage, check quality, and make governed changes under the same standards as engineers. Coalesce never stores data at rest, encrypts data in motion, and runs production refreshes inside your data platform. Leading data teams use Coalesce to resolve incidents 5× faster, deliver migrations and projects 4× faster, and reclaim 50%+ of warehouse compute.

Average Rating: 4.7/5.0

Total Reviews: 118

How Do G2 Users Rate Coalesce?

  • Ease of Use: 9.3/10 (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?

  • 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?

  • Who Uses This: Data Engineer, Analytics Engineer
  • Top Industries: Computer Software, Financial Services
  • Company Size: 63% Medium, 23% Large

What Do G2 Reviewers Say About Coalesce?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Coalesce, finding it intuitive and accessible for all experience levels.
  • Users value the automation capabilities of Coalesce, which significantly streamline data transformation and save time.
  • Users commend the excellent customer support of Coalesce, highlighting its responsiveness and dedication to user needs.
  • Users value the intuitive UI of Coalesce, making it easy for everyone to use and implement quickly.
  • Users love Coalesce for its user-friendly interface and seamless integration with Snowflake, enhancing data transformation efficiency.
Cons
  • Users often face UI design issues that hinder navigation and usability, affecting their overall experience with Coalesce.
  • Users face integration issues with Coalesce, particularly with limited options and challenges in managing git functionalities.
  • Users find the learning curve challenging due to the lack of comprehensive online courses and deep dives on features.
  • Users find limitations in credential management and customization options, hindering their overall experience with Coalesce.
  • Users find the poor documentation lacks depth, making it challenging to understand features and best practices effectively.

What Are Recent G2 Reviews of Coalesce?

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: 146

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

  • Ease of Use: 8.2/10 (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
  • LinkedIn® Page: www.linkedin.com
    2,473 employees on LinkedIn®
  • Ownership: NYSE: INFA

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 appreciate the automatic discovery and governance of data, enhancing data trust and operational efficiency across various platforms.
Cons
  • Users find integration issues challenging due to complex setups and potential performance slowdowns with large data environments.

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: 101

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
  • LinkedIn® Page: www.linkedin.com
    1,105 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Financial Services, Banking
  • Company Size: 73% Large, 18% Medium

What Do G2 Reviewers Say About Collibra?

AI-generated summary from verified user reviews

Pros
  • Users value the unified data intelligence platform of Collibra, enhancing collaboration and efficiency across teams and functions.
  • Users benefit from the ease of use of Collibra, which simplifies the management and governance of data assets.
  • Users value Collibra for its trustworthy and accessible data management, enhancing alignment between Business and IT teams.
  • Users value Collibra's enhanced data governance, appreciating its unified approach and powerful AI features for managing data assets.
  • Users appreciate the seamless integration of Collibra, enabling effective solutions tailored to their business needs.
Cons
  • Users experience limited functionality due to cumbersome navigation, unclear roles, and technical issues, hindering effective use of Collibra.
  • Users face complexity issues with Collibra, resulting in slow workflows and unclear roles that hinder effective collaboration.
  • Users find the complexity of Collibra hampers efficiency, causing confusion and slowdowns in their workflows.
  • Users find that improvement is needed for Collibra's verification process, system stability, and clarity of roles and responsibilities.
  • Users find the complex setup of Collibra challenging, requiring significant effort for onboarding and configuration.

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
  • 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?

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
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM
  • Phone: 1-866-277-7488

Who Uses This Product?

  • Company Size: 53% Large, 26% Medium

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

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
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM
  • Phone: 1-866-277-7488

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?

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?

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 find Select Star intuitive and user-friendly, simplifying data discovery and collaboration across complex datasets.
  • Users value the comprehensive data lineage features of Select Star, enhancing insights and metadata management effortlessly.
  • Users love the intuitive and user-friendly interface of Select Star, which streamlines data navigation and discovery.
  • Users value the intuitive usability of Select Star, enhancing data discovery and collaboration across teams effectively.
  • Users value the intuitive and user-friendly interface of Select Star, enhancing data discovery and collaboration significantly.
Cons
  • Users feel that Select Star's limited functionality restricts valuable insights like true user engagement and advanced reporting.
  • Users find Select Star's lineage visualization challenging with complex tables, affecting clarity and access to data.
  • Users find the complex setup for a dbt mesh ecosystem challenging, requiring significant effort to unify projects.
  • Users find the difficult learning curve of Select Star challenging, seeking more straightforward guidance for quicker value.
  • Users find the expertise required for Select Star challenging, seeking simpler guidance for quicker value realization.

What Are Recent G2 Reviews of Select Star?

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.