# Best Machine Learning Data Catalog Software - Page 5 *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)* Machine learning data catalogs allow companies to categorize, access, interpret, and collaborate around company data across multiple data sources, while maintaining a high level of governance and access management. Artificial intelligence is key to many features of machine learning data catalogs, enabling functionality such as machine learning recommendations, natural language querying, and dynamic data masking for enhanced security purposes. Companies can utilize machine learning data catalogs to maintain data sets in a single location so that searching for and discovering data is simple for everyday business users and analysts alike. Users have the ability to comment on, share, and recommend data sets so colleagues can have an immediate understanding of what they are querying. Additionally, IT administrators can put into place user provisioning to ensure unauthorized employees are not accessing sensitive data. Machine learning data catalogs are most frequently implemented by companies that have multiple data sources, are searching for one source of truth, and are attempting to scale data usage company-wide. These products are generally administered by IT departments, who can maintain organization and security, but data can be accessed by data scientists or analysts and the average business user. The data can then be transformed, modeled, and visualized either directly in the machine learning data catalog or through an integration with [business intelligence software](https://www.g2.com/categories/business-intelligence). It should be noted that not all machine learning data catalogs provide data preparation capabilities and may require an integration with a [business intelligence platform](https://www.g2.com/categories/business-intelligence-platforms). Additionally, these tools differ from [master data management software](https://www.g2.com/categories/master-data-management-mdm) due to their enhanced governance, collaboration, and machine learning functionality. To qualify for inclusion in the Machine Learning Data Catalog category, a product must: - Organize and consolidate data from all company sources in a single repository - Provide user access management for security and data governance purposes - Allow business users to search and access the data from within the catalog - Offer collaboration features around data sets, including categorizing, commenting, and sharing - Give intelligent recommendations based on machine learning for quicker access to relevant data ## How Many Machine Learning Data Catalog Software Products Does G2 Track? **Total Products under this Category:** 90 ### Category Stats (Jul 2026) - **Average Rating**: 4.38/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: erwin Data Modeler (+0.7%) - Among all products in this category, erwin Data Modeler recorded the largest rating increase compared to last month *Last updated: July 08, 2026* ## How Does G2 Rank Machine Learning Data Catalog Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 1,800+ Authentic Reviews - 90+ 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. ## Which Machine Learning Data Catalog Software Is Best for Your Use Case? - **Leader:** [Atlan](https://www.g2.com/products/atlan/reviews) - **Highest Performer:** [Collibra](https://www.g2.com/products/collibra/reviews) - **Easiest to Use:** [AWS Glue](https://www.g2.com/products/aws-glue/reviews) - **Top Trending:** [Atlan](https://www.g2.com/products/atlan/reviews) - **Best Free Software:** [Alation](https://www.g2.com/products/alation/reviews) --- **Sponsored** ### 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. 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. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1383&secure%5Bchosen_at%5D=2026-07-08T19%3A43%3A35Z&secure%5Bdisplayable_resource_id%5D=1383&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1383&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1886&secure%5Bresource_id%5D=1383&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmachine-learning-data-catalog&secure%5Btoken%5D=0158cf621b99c86310ac0a2f6fbb0b6464d48d266b5ebae3f000dd66f5d4dab5&secure%5Burl%5D=https%3A%2F%2Fwww.cloudera.com%2Fproducts%2Fcloudera-data-platform%2Fcdp-demos.html%3Finternal_link%3Dp18%23get-started&secure%5Burl_type%5D=custom_url) --- ## What Is Machine Learning Data Catalog Software? [IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure) ## What Software Categories Are Similar to Machine Learning Data Catalog Software? - [Data Quality Tools](https://www.g2.com/categories/data-quality) - [Data Governance Tools](https://www.g2.com/categories/data-governance-tools) - [Sensitive Data Discovery Software](https://www.g2.com/categories/sensitive-data-discovery) - [Data Fabric Software](https://www.g2.com/categories/data-fabric) - [DataOps Platforms](https://www.g2.com/categories/dataops-platforms) - [Active Metadata Management Software](https://www.g2.com/categories/active-metadata-management) - [Data Observability Software](https://www.g2.com/categories/data-observability) --- ## How Do You Choose the Right Machine Learning Data Catalog Software? ### What You Should Know About Healthcare Claims Management 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.