# Best Data Fabric Software

  *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*

   Data fabric software is a unified data platform that enables organizations to integrate their data and data management processes. Adopting a data fabric allows for the creation of complete views of their data, helping power existing processes and applications and enabling the rapid development of new use cases. A data fabric is not just a single solution but an entire data ecosystem that connects disparate data sources and infrastructure types across locations (on-premises, in the cloud, or hybrid environments), enabling analysis without onerous data integration requirements. The software offers benefits such as the ability to explore and extract value from any form of data regardless of location by connecting stores of structured and unstructured data. It provides centralized access via a single, unified view of an organization's data that inherits access and governance restrictions.

Companies use data fabric software to gain greater visibility into often highly complex and heterogeneous data landscapes. Data fabric software offers deeper insights and control over their data irrespective of where it sits, enabling better business decisions and strategies. Helping businesses become data-driven is key to the emergence of data fabric software and it can be adopted by any industry vertical. Fraud detection and security management, sales and marketing management, and governance and compliance management are some of the major use cases driving the growth of data fabric.

To qualify for inclusion in the Data Fabric category, a product must:

- Perform data management processes on a single unified platform
- Pull and connect or collaborate on data from disparate sources across locations
- Manage data across all environments (multi-cloud and on-premises)
- Allow single, seamless access and control to data across sources and types
- Provide analytics tools and connectivity to other analytical solutions
- Offer metadata functionality with data currency and data lineage capabilities





## Category Overview

**Total Products under this Category:** 76


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 1,800+ Authentic Reviews
- 76+ 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.


## Best Data Fabric Software At A Glance

- **Leader:** [ServiceNow Workflow Data Fabric](https://www.g2.com/products/servicenow-workflow-data-fabric/reviews)
- **Highest Performer:** [K2View](https://www.g2.com/products/k2view/reviews)
- **Easiest to Use:** [TimeXtender](https://www.g2.com/products/timextender/reviews)
- **Top Trending:** [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews)
- **Best Free Software:** [TimeXtender](https://www.g2.com/products/timextender/reviews)


---

**Sponsored**

### ER/Studio

What is ER/Studio? ER/Studio is an enterprise data modeling platform that helps organizations design, manage, and govern data assets across complex environments. It connects business requirements to technical implementation through conceptual, logical, and physical models, creating a single source of truth for enterprise data. Design and Collaborate Design data models and keep teams aligned with ER/Studio’s multi-user shared repository and web-based portal, Team Server. The centralized repository supports version control, role-based access, and parallel development so multiple modelers can work simultaneously while maintaining a complete change history. Team Server extends collaboration beyond architects by providing browser-based access for business and technical stakeholders to explore models, review definitions, and participate in discussions. Build, version, and review models on premises or across cloud-based platforms like Snowflake, Databricks, Azure Synapse, and Oracle to maintain accuracy, consistency, and visibility throughout your data ecosystem. Govern and Standardize Drive trusted analytics with standardized data definitions and integrated governance. ER/Studio connects business glossaries and data dictionaries while syncing seamlessly with Microsoft Purview and Collibra, ensuring consistent terminology, clear documentation, and enterprise-wide compliance from model creation to delivery. Accelerate with AI ER/Studio includes ERbert, an AI Data Modeling Assistant that converts plain-language business requests into structured data models. This feature saves time, reduces manual effort, and helps teams deliver faster. Why Organizations Choose ER/Studio - Intuitive interface preferred by data architects - Unified modeling environment for all major database platforms - Seamless collaboration between technical and business users - Enterprise-scale architecture for standardization and governance - Integrated AI and metadata management for faster delivery ER/Studio empowers enterprise data teams to build trusted, well-governed data architectures that accelerate analytics, reduce risk, and improve organizational understanding of data.



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---

## Top-Rated Products (Ranked by G2 Score)
### 1. [ServiceNow Workflow Data Fabric](https://www.g2.com/products/servicenow-workflow-data-fabric/reviews)
  Workflow Data Fabric is the AI‑ready data foundation of the ServiceNow AI Platform. It connects to any data—structured, unstructured, and streaming—contextualizes it with business meaning and governance, and controls it with lineage and policies so employees and AI agents can confidently act on real‑time information to prevent disruptions, resolve requests faster, and optimize operations—all on one platform. How Workflow Data Fabric turns data into instant action Connect Unify data from systems like Salesforce, SAP, Workday, data lakes, and event streams in real time without duplication or fragile point‑to‑point integrations. With Zero Copy Connectors, Stream Connect, External Content Connectors, and Integration Hub, WDF simplifies architecture and cuts integration cost and time. Contextualize Give data business meaning and make it trustworthy with an active Data Catalog, embedded governance, and lineage. Use Knowledge Graph to map relationships (e.g., customers, assets, orders) so AI agents and workflows understand context and make accurate decisions in the flow of work. Control Apply policies, permissions, and compliance guards across connected sources so the right people and AI agents access the right data, at the right time, with full auditability and traceability—no more shadow copies or opaque pipelines.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 103

**User Satisfaction Scores:**

- **Governance:** 8.7/10 (Category avg: 8.9/10)
- **Data Integration:** 9.2/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.0/10 (Category avg: 8.7/10)
- **Data Protection:** 8.8/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [ServiceNow](https://www.g2.com/sellers/servicenow)
- **Company Website:** https://www.servicenow.com/
- **Year Founded:** 2004
- **HQ Location:** Santa Clara, CA
- **Twitter:** @servicenow (54,215 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/29352/ (32,701 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Enterprise, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (37 reviews)
- Integrations (34 reviews)
- Automation (30 reviews)
- Efficiency Improvement (26 reviews)
- Data Management (25 reviews)

**Cons:**

- Complex Setup (23 reviews)
- Difficult Setup (17 reviews)
- Expensive (15 reviews)
- Slow Performance (14 reviews)
- Complexity (13 reviews)

### 2. [SAP Datasphere](https://www.g2.com/products/sap-datasphere/reviews)
  SAP Datasphere is a unified service for data integration, cataloging, semantic modeling, data warehousing, and virtualizing workloads across all your data. It enables every data professional to deliver seamless and scalable access to mission-critical business data. SAP Datasphere, and its open data ecosystem, is the foundation for a business data fabric.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 144

**User Satisfaction Scores:**

- **Governance:** 8.0/10 (Category avg: 8.9/10)
- **Data Integration:** 8.5/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)
- **Data Protection:** 8.1/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Company Website:** https://www.sap.com/
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,246 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Business Analyst, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 38% Enterprise, 37% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (43 reviews)
- Easy Integrations (33 reviews)
- Data Management (29 reviews)
- Analytics (22 reviews)
- Collaboration (21 reviews)

**Cons:**

- Slow Performance (25 reviews)
- Expensive (23 reviews)
- Performance Issues (23 reviews)
- Integration Issues (19 reviews)
- Complex Setup (17 reviews)

### 3. [TimeXtender](https://www.g2.com/products/timextender/reviews)
  With over 3,000 global customers, TimeXtender offers a comprehensive suite of products including Data Integration, Master Data Management, Data Quality, and Orchestration. These tools enable organizations to automate and streamline complex data processes, ensuring high data quality and governance across platforms. TimeXtender’s solutions are designed to help businesses efficiently manage their data assets without extensive coding, empowering informed decision-making and driving operational efficiency​​.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 146

**User Satisfaction Scores:**

- **Ease of Use:** 8.4/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [TimeXtender](https://www.g2.com/sellers/timextender)
- **Company Website:** https://www.timextender.com
- **Year Founded:** 2006
- **HQ Location:** Aarhus, Denmark
- **Twitter:** @TimeXtender (17,678 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/timextender/ (89 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Business Intelligence Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 46% Mid-Market, 32% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (67 reviews)
- Customer Support (33 reviews)
- Automation (25 reviews)
- Simple (23 reviews)
- Time-saving (22 reviews)

**Cons:**

- Limitations (18 reviews)
- Data Management (17 reviews)
- Poor Documentation (13 reviews)
- Steep Learning Curve (11 reviews)
- Error Reporting (9 reviews)

### 4. [K2View](https://www.g2.com/products/k2view/reviews)
  K2view Data Product Platform composes and delivers operational context as reusable data products to power use cases such as agentic AI, Customer 360, synthetic data generatio, data privacy and compliance, and test data management. Operational context represents complete, governed, real-time views of business entities such as customers, orders, and products, enabling consistent, trusted data for operational, analytical, and AI use cases. The platform integrates fragmented data from multiple sources into consistent, continuously updated data products, delivered on demand to downstream systems and users. Each data product is a self-contained unit that integrates and organizes multi-source data by entity, persists it in a high-performance Micro-Database, and governs it in-flight. It processes and enriches data in memory, continuously synchronizes it with source systems, and delivers it to authorized systems via APIs, SQL, messaging, CDC, MCP, and RAG. Core capabilities include: • K2Studio: Graphical tool for designing, creating, and deploying data products, accelerated by AI copilots • Universal Connectivity & Integration: Connect to any source or target (structured, semi-structured, unstructured) across cloud and on-prem, supporting batch and real-time, sync/async, and push/pull delivery • Augmented Data Catalog and Governance: AI-driven discovery and classification with in-flight enforcement of data privacy and data quality policies • Advanced Transformation: In-memory (RAM) data transformations and enrichment for near-real-time processing • AI & Agentic Enablement: Built-in MCP server per data product and ability to create data agents with planning, reasoning, and execution capabilities • Flexible Deployment: Cloud, on-prem, hybrid; supports fabric, mesh, hub architectures • K2Cloud Monitoring: Visibility into data product usage and SLAs


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 38

**User Satisfaction Scores:**

- **Governance:** 9.7/10 (Category avg: 8.9/10)
- **Data Integration:** 9.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.7/10)
- **Data Protection:** 9.7/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [K2View](https://www.g2.com/sellers/k2view)
- **Year Founded:** 2009
- **HQ Location:** Dallas, TX
- **Twitter:** @K2View (144 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1012853 (192 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Telecommunications, Information Technology and Services
  - **Company Size:** 38% Enterprise, 38% Small-Business


#### Pros & Cons

**Pros:**

- Data Management (3 reviews)
- Data Sharing (3 reviews)
- Ease of Use (3 reviews)
- Efficiency (3 reviews)
- Organization (3 reviews)

**Cons:**

- Complexity (3 reviews)
- Complex Setup (3 reviews)
- High Technical Requirement (3 reviews)
- Learning Curve (3 reviews)
- Learning Difficulty (3 reviews)

### 5. [Denodo](https://www.g2.com/products/denodo/reviews)
  Denodo is a leader in data management. The award-winning Denodo Platform is the leading logical data management platform for transforming data to trustworthy insights and outcomes for all data-related initiatives across the enterprise, including AI and self-service. Denodo's customers in all industries all over the world have delivered trusted AI-ready and business-ready data in a third of the time and with 10x better performance than with lakehouses and other mainstream data platforms alone. The Denodo Platform includes the following capabilities: - A semantic layer, with semantic search and embedded data prep in a self-service data catalog. - Unified, real-time-updated data views without expensive replication or copying of data. - Native connectors to over 200 source systems, both cloud and on-premises - An AI SDK which implements metadata-driven RAG (retrieval augmented generation) to provide trusted data to AI agents. - Query acceleration, improving lakehouse performance by 10x while also reducing compute and storage costs. - Federated enterprise-wide governance and privacy compliance. - Greater automation of common data engineering tasks, with the AI-powered Denodo Assistant. Enterprises world-wide across every major industry have used Denodo to achieve greater business self-service and agility, improve operational visibility and efficiency, optimize the performance and cost of modern data infrastructure such as Lakehouses, and ensure success of their AI initiatives. Denodo now offers two options to meet these needs: the Denodo Platform, deployable in all Clouds (AWS, Azure, GCP and Alibaba) and on-premises for full control, and Agora, our fully managed cloud service available on AWS, offering an entirely managed experience with the same rich data capabilities. Denodo provides a unique approach to data integration and management not found in any other platform. Denodo customers reported: 83% increase in business user productivity 67% reduction in time required to prepare data for AI 65% decrease in data delivery time vs. ETL 10x improvement in Lakehouse query performance compared to running queries directly resulting in an average three-year benefit of $6.8M, ROI of 408%, and payback within six months across customers.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 39

**User Satisfaction Scores:**

- **Governance:** 8.7/10 (Category avg: 8.9/10)
- **Data Integration:** 9.3/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.7/10)
- **Data Protection:** 8.8/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Denodo](https://www.g2.com/sellers/denodo)
- **Year Founded:** 1999
- **HQ Location:** Palo Alto, CA
- **Twitter:** @denodo (5,552 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/32150/ (777 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Information Technology and Services
  - **Company Size:** 47% Enterprise, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Functionality (3 reviews)
- Connectors (2 reviews)
- Data Cataloging (2 reviews)
- Data Integration (2 reviews)
- Ease of Use (2 reviews)

**Cons:**

- Expensive (2 reviews)
- Bug Issues (1 reviews)
- Bugs (1 reviews)
- Difficult Learning (1 reviews)
- Learning Curve (1 reviews)

### 6. [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews)
  For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Astronomer is the driving force behind Apache Airflow™, the de facto standard for expressing data flows as code. Airflow is downloaded more than 31 million times each month and is used by hundreds of thousands of teams around the world.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 135

**User Satisfaction Scores:**

- **Governance:** 8.0/10 (Category avg: 8.9/10)
- **Data Integration:** 8.5/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.7/10)
- **Data Protection:** 8.0/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Astronomer](https://www.g2.com/sellers/astronomer)
- **Company Website:** https://www.astronomer.io/
- **Year Founded:** 2018
- **HQ Location:** New York, US
- **Twitter:** @astronomerio (19,780 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10019299 (4,630 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Senior Data Engineer
  - **Top Industries:** Information Technology and Services, Financial Services
  - **Company Size:** 47% Mid-Market, 38% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (25 reviews)
- Efficiency Improvement (14 reviews)
- User Interface (13 reviews)
- Automation (11 reviews)
- Deployment Ease (10 reviews)

**Cons:**

- Expensive (8 reviews)
- Learning Difficulty (8 reviews)
- Learning Curve (6 reviews)
- Difficult Learning (5 reviews)
- Feature Limitations (5 reviews)

### 7. [Google Dataplex](https://www.g2.com/products/google-dataplex/reviews)
  Dataplex Break free from data silos with Dataplex’s intelligent data fabric that enables organizations to centrally discover, manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls, providing access to trusted data and powering analytics at scale.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 17

**User Satisfaction Scores:**

- **Governance:** 8.3/10 (Category avg: 8.9/10)
- **Data Integration:** 9.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.5/10 (Category avg: 8.7/10)
- **Data Protection:** 8.7/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,910,461 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 41% Small-Business, 41% Enterprise


### 8. [IBM Cloud Pak for Data](https://www.g2.com/products/ibm-cloud-pak-for-data/reviews)
  IBM Cloud Pak® for Data is a fully integrated data and AI platform that modernizes how businesses collect, organize and analyze data, forming the foundation to infuse AI across their organization. Running on Red Hat OpenShift and available on any cloud, this unified platform helps companies automate the end-to-end AI lifecycle. The intelligent data fabric in IBM Cloud Pak for Data enables automated distributed queries at scale without data movement; automated discovery and understanding of business-ready data; automated universal privacy and usage policies across the data ecosystem; and optimized model training, accuracy and explainability. View the demo: https://mediacenter.ibm.com/media/1\_je41fqqz. The platform delivers on the below use cases: • Data access and availability – Eliminate data silos and simplify your data landscape to enable faster, cost-effective extraction of value from your data. • Data quality and governance - Apply governance solutions and methodologies to deliver trusted, business data. • Data privacy and security - Fully understand and manage sensitive data with a pervasive privacy framework. • ModelOps - Automate the AI lifecycle and synchronize application and model pipelines to scale AI deployments. • AI governance – Ensure your AI is transparent, compliant and trustworthy with greater visibility into model development, with capabilities such as explainable AI, model risk management and bias detection. • AI for Financial Operations - Automate and integrate planning across your organization, from financial planning & analysis to workforce planning, sales forecasting and supply chain planning. • AI for Customer care - Reduce time to resolution, decrease call volume and increase customer satisfaction. IBM Watson Assistant (WA) can provide AI-powered automated assistance and enable human agents to better handle inquiries. IBM Watson Discovery (WD) complements Watson Assistant and can help unlock insights from complex business content. Discover IBM Cloud Pak for Data Industry Accelerators: https://dataplatform.cloud.ibm.com/gallery?context=cpdaas See a case study: https://mediacenter.ibm.com/media/1\_sr6lx8sz Try at no-cost: http://ibm.biz/dataplatformtrial


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 71

**User Satisfaction Scores:**

- **Governance:** 9.2/10 (Category avg: 8.9/10)
- **Data Integration:** 9.6/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)
- **Data Protection:** 9.6/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,390 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 51% Enterprise, 28% Small-Business


### 9. [Incorta](https://www.g2.com/products/incorta/reviews)
  Incorta is the first and only open data delivery platform that enables real-time analysis of live, detailed data across all systems of record—without the need for complex ETL processes. By enabling direct analysis on raw, source-identical data, Incorta provides faster, more accurate insights while removing barriers to exploration. With intuitive low-code/no-code tools, AI-powered querying through Nexus, and prebuilt business data applications, enterprise teams can quickly surface insights, break down technical roadblocks, and make smarter decisions without heavy engineering effort. For more information, please visit www.incorta.com.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 55

**User Satisfaction Scores:**

- **Governance:** 9.7/10 (Category avg: 8.9/10)
- **Data Integration:** 9.8/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.7/10)
- **Data Protection:** 8.8/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Incorta](https://www.g2.com/sellers/incorta)
- **Company Website:** https://www.incorta.com/
- **Year Founded:** 2013
- **HQ Location:** San Mateo, CA
- **LinkedIn® Page:** https://www.linkedin.com/company/incorta/ (325 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 56% Enterprise, 29% Mid-Market


#### Pros & Cons

**Pros:**

- Data Integration (1 reviews)
- Easy Integrations (1 reviews)
- Integrations (1 reviews)

**Cons:**

- Bugs (1 reviews)

### 10. [Progress MarkLogic](https://www.g2.com/products/progress-marklogic/reviews)
  Progress MarkLogic is an enterprise-grade multi-model data management platform that unlocks value from complex data. It works with the full breadth of a company's information and makes it easily discoverable and ready to power high-value applications, decision intelligence and trustworthy AI. Organizations leverage integrated capabilities to integrate, harmonize, search and visualize multi-model data to build a connected data ecosystem as the secure and scalable foundation for the AI era.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 64

**User Satisfaction Scores:**

- **Governance:** 8.3/10 (Category avg: 8.9/10)
- **Data Integration:** 8.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)
- **Data Protection:** 8.7/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Progress Software](https://www.g2.com/sellers/progress-software)
- **Company Website:** https://www.progress.com/
- **Year Founded:** 1981
- **HQ Location:** Burlington, MA.
- **Twitter:** @ProgressSW (48,845 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/progress-software/ (4,205 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 54% Enterprise, 25% Small-Business


### 11. [Cloudera Data Platform](https://www.g2.com/products/cloudera-cloudera-data-platform/reviews)
  At Cloudera, we believe data can make what is impossible today, possible tomorrow. We deliver an enterprise data cloud for any data, anywhere, from the Edge to AI. We enable people to transform vast amounts of complex data into clear and actionable insights to enhance their businesses and exceed their expectations. Cloudera is leading hospitals to better cancer cures, securing financial institutions against fraud and cyber-crime, and helping humans arrive on Mars — and beyond. Powered by the relentless innovation of the open-source community, Cloudera advances digital transformation for the world’s largest enterprises


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 131

**User Satisfaction Scores:**

- **Governance:** 7.5/10 (Category avg: 8.9/10)
- **Data Integration:** 6.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.7/10)
- **Data Protection:** 7.2/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Cloudera](https://www.g2.com/sellers/cloudera)
- **Year Founded:** 2008
- **HQ Location:** Palo Alto, CA
- **Twitter:** @cloudera (106,627 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/229433/ (3,387 employees on LinkedIn®)
- **Phone:** 888-789-1488

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 42% Enterprise, 32% Small-Business


### 12. [Elastic Stack](https://www.g2.com/products/elastic-stack/reviews)
  The Elastic Stack, commonly known as the ELK Stack, is a comprehensive suite of open-source tools designed for ingesting, storing, analyzing, and visualizing data in real-time. It comprises Elasticsearch, Kibana, Beats, and Logstash, enabling users to handle data from any source and in any format efficiently. Key Features and Functionality: - Elasticsearch: A distributed, JSON-based search and analytics engine that allows for rapid storage, search, and analysis of large volumes of data. - Kibana: An extensible user interface that provides powerful visualizations, dashboards, and management tools to interpret and present data effectively. - Beats and Logstash: Data ingestion tools that collect and process data from various sources, transforming and forwarding it to Elasticsearch for indexing. - Integrations: A multitude of pre-built integrations that facilitate seamless data collection and connection with the Elastic Stack, enabling quick insights. Primary Value and User Solutions: The Elastic Stack empowers organizations to harness the full potential of their data by providing a scalable and resilient platform for real-time search and analytics. It addresses challenges such as managing large datasets, ensuring high availability, and delivering relevant search results swiftly. By offering a unified solution for data ingestion, storage, analysis, and visualization, the Elastic Stack enables users to gain actionable insights, enhance operational efficiency, and make informed decisions based on their data.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 97

**User Satisfaction Scores:**

- **Governance:** 9.4/10 (Category avg: 8.9/10)
- **Data Integration:** 9.4/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.0/10 (Category avg: 8.7/10)
- **Data Protection:** 9.4/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Elastic](https://www.g2.com/sellers/elastic)
- **Year Founded:** 2012
- **HQ Location:** San Francisco, CA
- **Twitter:** @elastic (64,544 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/814025/ (4,986 employees on LinkedIn®)
- **Ownership:** NYSE: ESTC

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 45% Mid-Market, 34% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (3 reviews)
- Flexibility (3 reviews)
- Log Management (3 reviews)
- Search Efficiency (3 reviews)
- Versatility (3 reviews)

**Cons:**

- Resource Management (3 reviews)
- Complexity Issues (2 reviews)
- Expensive (2 reviews)
- High Memory Usage (2 reviews)
- Learning Curve (2 reviews)

### 13. [Cinchy](https://www.g2.com/products/cinchy-cinchy/reviews)
  With the Cinchy Data Collaboration platform liberate data from applications and control and connect as data products in a network, eliminating the need for future data integration. Build a more agile data ecosystem that makes change simple, rapidly accelerates business outcomes, and fosters collaborative intelligence across your enterprise. With Cinchy, cure the pains of integration to deliver projects at half the cost and time, improve data governance with universal data access controls, and provide data access to business users for self-serve reporting and analytics.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 19

**User Satisfaction Scores:**

- **Governance:** 9.8/10 (Category avg: 8.9/10)
- **Data Integration:** 9.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.7/10)
- **Data Protection:** 10.0/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Cinchy](https://www.g2.com/sellers/cinchy)
- **Year Founded:** 2017
- **HQ Location:** Toronto, ON
- **Twitter:** @itscinchy (462 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/16200640/ (38 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services
  - **Company Size:** 53% Mid-Market, 37% Enterprise


### 14. [TIBCO Data Fabric](https://www.g2.com/products/tibco-data-fabric/reviews)
  Data virtualization breaks down data silos delivering one place to access, combine, and provision all your data. Business-friendly data views simplify access and hide IT complexity. Agile data engineering speeds time-to-solution, reduces costs, and delivers the most up-to-date and complete information to your business.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 10

**User Satisfaction Scores:**

- **Governance:** 7.9/10 (Category avg: 8.9/10)
- **Data Integration:** 8.1/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.5/10 (Category avg: 8.7/10)
- **Data Protection:** 7.9/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Cloud Software Group](https://www.g2.com/sellers/cloud-software-group)
- **HQ Location:** Fort Lauderdale, FL
- **Twitter:** @cloudsoftware (122 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cloudsoftwaregroup/ (9,677 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 60% Enterprise, 40% Mid-Market


### 15. [Talend Data Fabric](https://www.g2.com/products/talend-data-fabric/reviews)
  Talend Data Fabric is a unified platform that enables you to manage all your enterprise data within a single environment. Leverage all the cloud has to offer to manage your entire data lifecycle – from connecting the broadest set of data sources and platforms to intuitive self-service data access.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 62

**User Satisfaction Scores:**

- **Governance:** 8.3/10 (Category avg: 8.9/10)
- **Data Integration:** 8.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)
- **Data Protection:** 8.1/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik (64,263 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,529 employees on LinkedIn®)
- **Phone:** 1 (888) 994-9854

**Reviewer Demographics:**
  - **Company Size:** 45% Mid-Market, 28% Enterprise


#### Pros & Cons

**Pros:**

- Data Management (3 reviews)
- Data Integration (2 reviews)
- Ease of Use (2 reviews)
- Flexibility (2 reviews)
- Performance (2 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Expensive (3 reviews)
- UX Improvement (3 reviews)
- Poor Documentation (2 reviews)
- Slow Performance (2 reviews)

### 16. [Mosaic.](https://www.g2.com/products/cma-consulting-mosaic/reviews)
  Mosaic is the art of data management. Create your bigger picture with our Mosaic line of data products. It offers trusted tools that optimize the way you discover, evaluate, and visualize quality insights.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Governance:** 8.3/10 (Category avg: 8.9/10)
- **Data Integration:** 7.1/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.7/10)
- **Data Protection:** 7.9/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [CMA Consulting](https://www.g2.com/sellers/cma-consulting)
- **Year Founded:** 1984
- **HQ Location:** Latham, NY
- **Twitter:** @CMACorp (225 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/228012 (422 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 42% Mid-Market


### 17. [Tengu DataOps platform](https://www.g2.com/products/tengu-dataops-platform/reviews)
  TENGU originated in 2016 as a solution to automate non-value generating tasks and more quickly set up data architectures, so there would be more time to dedicate to gaining actionable insights. In 2020 TENGU became a fully functional DataOps platform with many more functions and capabilities. TENGU is a DataOps platform for data-driven companies, that enables them to improve the efficiency of data scientists, analysts and other profiles inside the company. It enables them to focus on business intelligence instead of data operations. TENGU is available through a Platform-as-a-Service license (TENGU.CORE or TENGU.PLUS) and connects to any company infrastructure (e.g. GCE, AWS, Azure, VMWare, OpenStack, MaaS). It enables teams to work more efficiently with the most useful data and collaborate better, giving a boost to your business value and revenue growth.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Governance:** 9.0/10 (Category avg: 8.9/10)
- **Data Integration:** 9.2/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.6/10 (Category avg: 8.7/10)
- **Data Protection:** 9.4/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Tengu](https://www.g2.com/sellers/tengu)
- **Year Founded:** 2016
- **HQ Location:** Gent, (Oost-Vlaanderen)
- **LinkedIn® Page:** https://www.linkedin.com/company/10862558 (2 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 50% Mid-Market, 38% Small-Business


### 18. [SyncWith](https://www.g2.com/products/syncwith/reviews)
  Access 1000s of APIs and get the data you need.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Governance:** 7.8/10 (Category avg: 8.9/10)
- **Data Integration:** 9.4/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.7/10)
- **Data Protection:** 9.0/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [SyncWith](https://www.g2.com/sellers/syncwith)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/syncwith-data (6 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 73% Small-Business, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Easy Setup (1 reviews)

**Cons:**

- Bugs (1 reviews)

### 19. [Atlan](https://www.g2.com/products/atlan/reviews)
  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:** 125

**User Satisfaction Scores:**

- **Governance:** 8.3/10 (Category avg: 8.9/10)
- **Data Integration:** 8.3/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.7/10)
- **Data Protection:** 7.5/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Atlan](https://www.g2.com/sellers/atlan)
- **Year Founded:** 2019
- **HQ Location:** New York, US
- **Twitter:** @AtlanHQ (9,732 Twitter followers)
- **LinkedIn® Page:** https://in.linkedin.com/company/atlan-hq (580 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Computer Software
  - **Company Size:** 53% Mid-Market, 40% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (18 reviews)
- User Interface (12 reviews)
- Features (11 reviews)
- Data Lineage (10 reviews)
- Easy Setup (10 reviews)

**Cons:**

- Learning Curve (5 reviews)
- Limited Functionality (5 reviews)
- User Interface Issues (5 reviews)
- Difficult Learning (4 reviews)
- Integration Issues (4 reviews)

### 20. [ILUM](https://www.g2.com/products/ilum-ilum/reviews)
  Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 23

**User Satisfaction Scores:**

- **Governance:** 10.0/10 (Category avg: 8.9/10)
- **Data Integration:** 10.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.7/10)
- **Data Protection:** 10.0/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Ilum](https://www.g2.com/sellers/ilum)
- **Company Website:** https://ilum.cloud/
- **Year Founded:** 2019
- **HQ Location:** Santa Fe, US
- **Twitter:** @IlumCloud (19 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/ilum-cloud/ (4 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Telecommunications
  - **Company Size:** 52% Enterprise, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (17 reviews)
- Features (17 reviews)
- Integrations (17 reviews)
- Setup Ease (16 reviews)
- Easy Integrations (15 reviews)

**Cons:**

- Complex Setup (9 reviews)
- Difficult Setup (9 reviews)
- Learning Curve (9 reviews)
- UX Improvement (8 reviews)
- Complexity (7 reviews)

### 21. [Environments as a Service](https://www.g2.com/products/environments-as-a-service/reviews)
  Uffizzi drives 20-50% improvements in development velocity by empowering software development teams with ephemeral environments (also known as preview environments). By providing these environments "on-demand" for Development, QA, Staging, and Demos, Uffizzi enables teams to overcome shared environments bottlenecks, meet capacity in real time, eliminate manual set-up for DevOps personnel, and unlock huge efficiencies at scale. Uffizzi offers a fully containerized solution that is the most cost effective, lightweight, and fast environments as a service solution on the market. Trusted by Open Source and some of the world's most innovative software teams at Spotify, NocoDB, Dev.to, Meilisearch and many others.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 8

**User Satisfaction Scores:**

- **Governance:** 7.6/10 (Category avg: 8.9/10)
- **Data Integration:** 8.8/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.7/10)
- **Data Protection:** 7.9/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Uffizzi](https://www.g2.com/sellers/uffizzi)
- **Year Founded:** 2021
- **HQ Location:** Nashville, US
- **Twitter:** @Uffizzi_ (1,414 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/uffizzi-cloud/ (3 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Accounting
  - **Company Size:** 63% Enterprise, 25% Mid-Market


### 22. [Anzo](https://www.g2.com/products/anzo/reviews)
  Altair® Graph Studio™ (formerly Anzo) is a comprehensive data discovery and integration toolset that applies a semantic, graph-based data fabric layer over diverse enterprise data sources. This semantic layer adds real-world meaning to data – structured and unstructured alike – making insights clearer and previously unseen connections explicit. Graph Studio creates a single pane of glass for an enterprise's data, eliminates data silos, activates unused data, and enables exciting new levels of on-demand business insight.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Governance:** 9.2/10 (Category avg: 8.9/10)
- **Data Integration:** 10.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.4/10 (Category avg: 8.7/10)
- **Data Protection:** 9.2/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Cambridge Semantics](https://www.g2.com/sellers/cambridge-semantics)
- **Year Founded:** 2007
- **HQ Location:** Boston, Massachusetts
- **LinkedIn® Page:** https://www.linkedin.com/company/202709 (24 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 45% Small-Business, 36% Mid-Market


### 23. [Fraxses](https://www.g2.com/products/fraxses/reviews)
  Fraxses by Intenda is a data management platform used by organisations across multiple industries. It enables organisations to connect all their data sources in a single ecosystem, with full lineage and pedigree. While the data sources may be in different locations and utilise different technologies, users experience them as part of the same virtual environment. Fraxses can connect to virtually any data source, and can process structured and unstructured data. Leveraging data virtualisation, Fraxses advances a connect, not collect approach, serving data direct from the source in real-time. As an end-to-end solution, the platform frees businesses from having to maintain and manage multiple products, and provides the ideal framework on which to build a data fabric. Fraxses comprises various modules that are used for different functions: Legoz – the platform’s front-end provides the intuitive user interface that makes it easy for business users to engage with data. Insights - this data visualisation module incorporates powerful business analytics technology with a proven track record in the embedded BI space. Integrate – facilitates all types of data integration, including real-time and batch processing, and supports both ETL and ELT. Opus – Intenda’s development teams use this low-code/no-code application development platform to create bespoke solutions and custom applications. When businesses invest in Fraxses, they are assured of a swift ROI, and a futureproof solution to meet tomorrow’s data demands.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 6

**User Satisfaction Scores:**

- **Governance:** 9.4/10 (Category avg: 8.9/10)
- **Data Integration:** 9.6/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.7/10)
- **Data Protection:** 9.4/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [Intenda](https://www.g2.com/sellers/intenda)
- **Year Founded:** 2001
- **HQ Location:** Johannesburg, ZA
- **Twitter:** @Intenda (50 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/intenda/ (94 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 50% Mid-Market


### 24. [Scikiq: Comprehensive Data Management Platform](https://www.g2.com/products/scikiq-comprehensive-data-management-platform/reviews)
  SCIKIQ help make AI possible for enterprises. SCIKIQ brings together everything an enterprise needs to scale AI, clean data, trusted governance, semantic context, real-time orchestration, and intelligent agents, all in one platform. SCIKIQ brings Data Hub, a Unified Data Layer, a foundational architecture that creates a single version of truth across all data sources, departments, and use cases within the enterprise. SCIKIQ Data Hub brings Data integration, Processing and Curation, Data Governance and Data Visualisation all in one platform. It also brings Gen AI studio, Agentic AI and Auto ML studio for your AI Initiatives. Our Conversation (Self Service) Analytics, prompt to process (data analytics, Dashboards, Agents, insights & products) AI Co-pilot is among the best in the world Recognized by Forrester as top 34 AI enabled platform in the world, By NASSCOM in top 10 in Deep Tech club and By YourStory and Inc Media in Top 30 companies to watch. Selected by AWS to showcase at MWC and AWS re:Invent


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Governance:** 10.0/10 (Category avg: 8.9/10)
- **Data Integration:** 9.4/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.4/10 (Category avg: 8.7/10)
- **Data Protection:** 10.0/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [SCIKIQ](https://www.g2.com/sellers/scikiq)
- **Year Founded:** 2023
- **HQ Location:** Gurgaon
- **Twitter:** @scikiq (9 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/SCIKIQ (29 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 36% Enterprise, 36% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (4 reviews)
- Data Management (3 reviews)
- Functionality (3 reviews)
- Data Integration (2 reviews)
- Easy Setup (2 reviews)

**Cons:**

- Complexity Issues (2 reviews)
- Complexity (1 reviews)
- Data Management (1 reviews)
- Expensive (1 reviews)
- Integration Issues (1 reviews)

### 25. [Cluedin](https://www.g2.com/products/cluedin/reviews)
  CluedIn is the modern Master Data Management platform that accelerates the time it takes to prepare data for insight by up to 80%. Our zero-modeling, schemaless approach means that data stewards no longer need to spend time manually modeling, mapping and integrating their data, allowing the natural data model to emerge and evolve with your business. Not only does this save time and money, it also means that the quality of your data can improve by up to 50% in as little as six weeks. Our system integrates with over 27 Microsoft Azure Data and Analytics Services, including Purview and the Fabric suite. We were also the first MDM vendor to integrate with Azure OpenAI and to launch our own AI Assistant. With CluedIn, integrate data 60% faster, validate and deduplicate data 50% quicker, and raise analytics accuracy by 25%.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Governance:** 7.8/10 (Category avg: 8.9/10)
- **Data Integration:** 6.1/10 (Category avg: 8.8/10)
- **Ease of Use:** 7.9/10 (Category avg: 8.7/10)
- **Data Protection:** 7.2/10 (Category avg: 8.9/10)


**Seller Details:**

- **Seller:** [CluedIn](https://www.g2.com/sellers/cluedin)
- **Year Founded:** 2015
- **HQ Location:** Copenhagen, Copenhagen
- **Twitter:** @cluedinhq (2,434 Twitter followers)
- **LinkedIn® Page:** http://www.linkedin.com/company/cluedin-aps (41 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 42% Mid-Market, 33% Enterprise


#### Pros & Cons

**Pros:**

- AI Features (1 reviews)
- Customer Support (1 reviews)
- Easy Setup (1 reviews)

**Cons:**

- Learning Curve (1 reviews)



## Parent Category

[IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure)



## Related Categories

- [Big Data Integration Platforms](https://www.g2.com/categories/big-data-integration-platforms)
- [Machine Learning Data Catalog Software](https://www.g2.com/categories/machine-learning-data-catalog)
- [Data Governance Tools](https://www.g2.com/categories/data-governance-tools)



---

## Buyer Guide

### What You Should Know About Data Fabric Software

### What is Data Fabric Software?

Data fabric software is an architecture that connects sources, types, and the location of data and provides end-to-end data integration. It is a unified environment for data services and technologies, helping with data management. Using this platform, organizations can collect enterprise data from disparate sources and provide it to various teams within the company without external help. The data is pulled by APIs from data warehouses, data lakes, databases, and apps. Data fabric software can be enhanced by incorporating artificial intelligence (AI) or machine learning (ML). AI-powered versions of these tools provide personalized recommendations to select datasets which can boost the speed of data science projects. 

Data assets are usually generated in silos, while data preparation cycles in the data pipeline are long and take up a lot of time, affecting an organization’s data optimization. Data fabric systems help standardize data management practices across cloud, on-premises, and edge services. These tools usually include various data management technologies like [data catalog](https://www.g2.com/categories/machine-learning-data-catalog), [governance](https://www.g2.com/categories/data-governance), virtualization, integration, pipeline, and orchestration. Data fabric software helps users access data using unique workflows while also democratizing data, allowing data citizens to access information across the organization. Using this tool gives companies a holistic view of the business process.

### What are the Common Features of Data Fabric Software?

The following are some core features of data fabric software that can help users in various ways:

**Unified data environment:** Data fabric software creates an architecture that integrates various data management processes like data collaboration, data discovery, data analytics, data visualization, data access, and data control on a single platform. This eliminates the need for multiple data integration products.

**Data collaboration and sharing:** Data fabric software allows data connectivity into a single unified view, helping data to be accessed by or shared with internal and external applications.

**Governance and compliance:** Data owners remain in full control of who can visit, edit, download, or query their datasets. Data fabric software enables compliance, preserves integrity, and controls access. These tools also incorporate data quality in each step of data management.

**Environment agnostic:** Data fabric software allows data management across multiple environments such as on-premises, in the cloud, hybrid, and multi cloud.

**Metadata management:** Data fabric has data lineage capabilities and currency of data, which means it contains data migration and transformation history. The currency of data defines the state of the data—active or archived.

**Data analytics and visualization:** These tools use continuous analytics over the existing metadata assets for better business insights.

### What are the Benefits of Data Fabric Software?

While there are many data management technologies like master data management, data hubs, and data lakes, data fabric differs from them in various ways. 

**Enhanced data management:** Data fabric software helps retrieve, validate, and enrich data automatically. It helps in enterprise data integration and management. It also helps to provide a single unified view of the data, which allows end users to identify and track data easily and use it efficiently. Automation and integration help in dynamic data orchestration across a distributed ecosystem.

**Easy to use:** Technical and non-technical users can use data fabric platforms. The architecture makes it possible to create various user interfaces. Business users can create sleek dashboards and use it for various other functions, while data scientists can also use it for deep data exploration.

**Compatible with hybrid hosting environments:** Data fabrics are environment agnostic. It can help in bi-directional integration with almost all the components to create a fabric-like structure and eliminate the need for coding. Data fabric software supports on-premises, hybrid cloud, and multi-cloud environments.

**High scalability:** Data fabric systems can manage data at an enterprise scale. It helps to ingest data automatically, which would typically remain unutilized. They are scalable with minimum interference and no investment requirement into expensive hardware or trained staff. The data architecture helps reduce big data complexity and ultimately drives strategic business outcomes.

**Fast insights:** Automation of data engineering tasks and integration augmentation helps deliver real-time insights faster. Also, continuous data analytics used by data fabric also helps provide value through rapid access. Data fabric software combines data warehouses and data lakes and integrated data from multiple apps, providing services that help organizations monitor and control their data.

**Seamless integration:** Data fabric software solves the common challenge of big data in organizations. This tool removes data silos through a holistic approach and helps in the seamless integration of data across various functions. Many workloads are moving to the cloud, and it requires data. Data fabric software streamlines this movement from the cloud to the data center or between hybrid clouds. 

### Who Uses Data Fabric Software?

Data fabric platforms have various stakeholders within an organization. 

**Data scientists:** Data scientists use data fabric software to explore deep and hidden enterprise data to share with other departments for actionable insights.

**Business users:** The organization's business users, like marketers, can use these tools to make critical business decisions. Smart data fabric solutions are the emerging data architecture helping organizations fast-track their enterprise data initiatives. 

#### Software Related to Data Fabric Software

Following are some tools that can be used with data fabric software:

[Machine learning data catalog software](https://www.g2.com/categories/machine-learning-data-catalog) **:** Machine learning data catalogs allow organizations to categorize, access, interpret, and collaborate data across multiple data sources and maintain a high level of governance and access management. Data fabric helps identify, collect, and analyze data sources and metadata. 

[Data quality software](https://www.g2.com/categories/data-quality) **:** Data quality software uses a set of technologies to identify, understand, prevent, and correct issues with the data used for decision making. Data quality tools carry out critical functions like data profiling, parsing, standardization, cleansing, built-in workflow, and knowledge bases. 

[Data governance software](https://www.g2.com/categories/data-governance) **:** Data governance software is used to enforce data-related policies. These products help establish guidelines, processes, and accountability measures to ensure data quality standards are met. Data governance tools enable organizations to develop a framework to know what data they own and how to use it optimally. 

[Data preparation software](https://www.g2.com/categories/data-preparation) **:** Data preparation and delivery are important steps in data transformation and integration during the data pipeline lifecycle. Data preparation begins with loading data into a data platform from a data lake. Then data processing begins using extract, transform, load or extract, load, transform (ETL or ELT) tools. The result is prepared data.

### Challenges with Data Fabric Software

Although data fabric systems aim at data management, there are some challenges when implementing its services. Below are a few challenges faced by organizations commonly:

**Deployment and configuration of services:** Services may have to be deployed on multiple servers to optimize performance. This may require configuring services in specific ways for them to work together. 

**Creating a data model and managing data:** A data model determines how data will be structured and organized. Thus it becomes necessary to build a data model that fulfills the organization's needs and can be managed easily. Data fabric unifies data across various data types and points using a semantic knowledge graph. One of the challenges is managing and saving data. Data is available in different formats; hence, the software must be able to handle and manage all kinds of data. Building an architecture that supports different environments is a challenge.

**Integration with external systems:** Data fabric makes it possible to integrate with multiple systems. For integration with external systems, middleware software is usually created to mediate between these external systems and data fabric tools, managing their communication. The challenge here is that two communicating systems may have different architectures; thus, it is challenging to produce a single middleware.

**Data security:** Data protection is paramount to any organization. One of the challenges, when data is being transferred from one point to another using data fabric tools, is that the data is vulnerable to attacks. However, this can be avoided by introducing firewalls to ensure safety. It is also essential to go beyond data masking and encryption to ensure total data protection.

### How to Buy Data Fabric Software?

#### Requirements Gathering (RFI/RFP) for Data Fabric Software

Data fabric software solves several data management concerns or challenges in an organization. Before purchasing data fabric software, it is important to understand the existing requirements of the organization. If an organization needs only deduplication and data validation, a data quality tool may help. Many organizations also choose data processing solutions such as ETL tools to process and integrate their data. Depending on where in the organization there is a need for data management, data fabric solutions can be chosen.

#### Compare Data Fabric Software Products

**Create a long list**

A list of data fabric software vendors can help understand their offerings. The team in the organization can then evaluate the vendors that would fulfill the organization’s needs. 

**Create a short list**

After evaluating various data fabric solutions, the organization's decision makers can shortlist a few depending on which vendors fit the bill.

**Conduct demos**

After shortlisting vendors, companies should look for a demo. The demo gives a better understanding of the technical functionality of the software. Nowadays, data fabric tools come with artificial intelligence features. AI-based recommendations help faster data recovery. These could be some important features that the teams need to know. IT professionals, data scientists, as well as data management and business teams can attend the demo to evaluate the product from various perspectives. 

#### Selection of Data Fabric Software

**Choose a selection team**

A selection team is a mix of technical users and business users like data scientists, data management teams, and marketing teams. Along with that, the team should have a key decision maker.

**Negotiation**

Once a vendor is selected for their software, it is advisable to understand their pricing and negotiate if necessary. The negotiation part entirely depends on the organization’s budget and the difference between the product pricing and the budget. 

**Final decision**

After both parties arrive at a mutually agreeable term, it is time to decide whether to buy the software.