# Best Enterprise Data Fabric Software

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


   Products classified in the overall Data Fabric category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Data Fabric to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Data Fabric category.

In addition to qualifying for inclusion in the Data Fabric Software category, to qualify for inclusion in the Enterprise Business Data Fabric Software category, a product must have at least 10 reviews left by a reviewer from an enterprise business.




  
## Top Data Fabric Software at a Glance
| # | Product | Rating | Best For | What Users Say |
|---|---------|--------|----------|----------------|
| 1 | [ServiceNow Workflow Data Fabric](https://www.g2.com/products/servicenow-workflow-data-fabric/reviews) | 4.3/5.0 (138 reviews) | Zero-copy data access inside ServiceNow workflows | "[Zero-Copy, Real-Time Intelligence with ServiceNow Workflow Data Fabric](https://www.g2.com/survey_responses/servicenow-workflow-data-fabric-review-12543653)" |
| 2 | [SAP Datasphere](https://www.g2.com/products/sap-datasphere/reviews) | 4.2/5.0 (166 reviews) | SAP-native federated data fabric with semantic preservation | "[SAP Datasphere A Powerful Platform for Data Integration and Real-Time Business Insights](https://www.g2.com/survey_responses/sap-datasphere-review-12817894)" |
| 3 | [K2View](https://www.g2.com/products/k2view/reviews) | 4.6/5.0 (43 reviews) | Entity-based data fabric for real-time access | "[A Practical Solution for Managing Enterprise Data at Scale](https://www.g2.com/survey_responses/k2view-review-12928262)" |
| 4 | [Discovery Hub](https://www.g2.com/products/discovery-hub/reviews) | 4.3/5.0 (147 reviews) | Low-code data warehouse automation with lineage | "[Effortless Data Consistency and Automation](https://www.g2.com/survey_responses/discovery-hub-review-12028087)" |
| 5 | [Denodo](https://www.g2.com/products/denodo/reviews) | 4.3/5.0 (39 reviews) | Federated data virtualization across heterogeneous sources | "[Effortless Data Virtualization with Top-Notch Security](https://www.g2.com/survey_responses/denodo-review-12582329)" |
| 6 | [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews) | 4.5/5.0 (135 reviews) | Managed Airflow orchestration with infrastructure abstraction | "[Excellent developer and customer experience](https://www.g2.com/survey_responses/astro-by-astronomer-review-8428848)" |
| 7 | [Incorta](https://www.g2.com/products/incorta/reviews) | 4.4/5.0 (55 reviews) | Multi-source ELT with zero-transformation analytics | "[Facilitating presentation and information access](https://www.g2.com/survey_responses/incorta-review-9467627)" |
| 8 | [Google Cloud Knowledge Catalog](https://www.g2.com/products/google-cloud-knowledge-catalog/reviews) | 4.3/5.0 (17 reviews) | Cross-silo data fabric with unified governance | "[Must have tool for businesses that gather data in ocean](https://www.g2.com/survey_responses/google-cloud-knowledge-catalog-review-8951605)" |
| 9 | [IBM Cloud Pak for Data](https://www.g2.com/products/ibm-cloud-pak-for-data/reviews) | 4.3/5.0 (72 reviews) | Unified data fabric with AI-governed virtualization | "[Comprehensive solution for data-intensive workflows](https://www.g2.com/survey_responses/ibm-cloud-pak-for-data-review-12967373)" |
| 10 | [Progress MarkLogic](https://www.g2.com/products/progress-marklogic/reviews) | 4.3/5.0 (65 reviews) | Multi-model unstructured data fabric with enterprise search | "[I am technical support engineer working with Microsoft with azure identity](https://www.g2.com/survey_responses/progress-marklogic-review-9456409)" |

  
## How Many Data Fabric Software Products Does G2 Track?
**Total Products under this Category:** 78

### Category Stats (Jun 2026)
- **Average Rating**: 4.33/5 (↑0.01 vs May 2026) The average rating of products in this category, based on all submitted ratings
- **New Reviews This Quarter**: 90
- **Buyer Segments**: Enterprise 45% │ Small-Business 27% │ Mid-Market 27% Represents the distribution of reviewers across all products in this category.
- **Top Trending Product**: K2View (+0.26%) - Among all products in this category, K2View recorded the largest rating increase compared to last month
*Last updated: June 18, 2026*

  
## How Does G2 Rank Data Fabric Software Products?

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

- 30 Analysts and Data Experts
- 1,800+ Authentic Reviews
- 78+ 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.

  
  
---

**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.



[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=2394&secure%5Bdisplayable_resource_id%5D=1661&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=1661&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=6093&secure%5Bresource_id%5D=2394&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-fabric%2Fenterprise&secure%5Btoken%5D=4cdb0ad25f9a488a6063aa618b0e744219849aceb2ca512f9699f2f736946d33&secure%5Burl%5D=https%3A%2F%2Ferstudio.com%2Ftransform-your-data%2F%3Futm_campaign%3DERS-G2%26utm_medium%3Dreferral%26utm_source%3Dg2%26utm_content%3DERS-G2-CatagoryCampaign-transform-your-data&secure%5Burl_type%5D=custom_url)

---

  ## What Are the Top-Rated Data Fabric Software Products in 2026?
### 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:** 138
**How Do G2 Users Rate ServiceNow Workflow Data Fabric?**

- **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)

**Who Is the Company Behind ServiceNow Workflow Data Fabric?**

- **Seller:** [ServiceNow](https://www.g2.com/sellers/servicenow)
- **Company Website:** https://www.servicenow.com/
- **Year Founded:** 2004
- **HQ Location:** Santa Clara, CA
- **Twitter:** @servicenow (55,548 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/29352/ (35,081 employees on LinkedIn®)

**Who Uses This Product?**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 45% Enterprise, 27% Mid-Market


#### What Are ServiceNow Workflow Data Fabric's Pros and 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:** 166
**How Do G2 Users Rate SAP Datasphere?**

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

**Who Is the Company Behind SAP Datasphere?**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Company Website:** https://www.sap.com/
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,052 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,955 employees on LinkedIn®)

**Who Uses This Product?**
  - **Who Uses This:** Software Engineer, Business Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 40% Enterprise, 35% Mid-Market


#### What Are SAP Datasphere's Pros and 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. [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:** 43
**How Do G2 Users Rate K2View?**

- **Governance:** 8.8/10 (Category avg: 8.9/10)
- **Data Integration:** 9.8/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.7/10)
- **Data Protection:** 9.0/10 (Category avg: 8.9/10)

**Who Is the Company Behind K2View?**

- **Seller:** [K2View](https://www.g2.com/sellers/k2view)
- **Year Founded:** 2009
- **HQ Location:** Dallas, TX
- **Twitter:** @K2View (142 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1012853 (192 employees on LinkedIn®)

**Who Uses This Product?**
  - **Top Industries:** Telecommunications, Information Technology and Services
  - **Company Size:** 43% Enterprise, 34% Small-Business


#### What Are K2View's Pros and 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)

### 4. [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:** 72
**How Do G2 Users Rate IBM Cloud Pak for Data?**

- **Governance:** 9.3/10 (Category avg: 8.9/10)
- **Data Integration:** 9.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)
- **Data Protection:** 9.7/10 (Category avg: 8.9/10)

**Who Is the Company Behind IBM Cloud Pak for Data?**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity (74,660 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Who Uses This Product?**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 51% Enterprise, 28% Small-Business


### 5. [Cloudera](https://www.g2.com/products/cloudera/reviews)
  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.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 131
**How Do G2 Users Rate Cloudera?**

- **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)

**Who Is the Company Behind Cloudera?**

- **Seller:** [Cloudera](https://www.g2.com/sellers/cloudera)
- **Company Website:** https://www.cloudera.com
- **Year Founded:** 2008
- **HQ Location:** Santa Clara, CA
- **Twitter:** @cloudera (106,442 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/229433/ (3,446 employees on LinkedIn®)

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


#### What Are Cloudera's Pros and Cons?

**Pros:**

- Ease of Use (22 reviews)
- Scalability (17 reviews)
- Security (9 reviews)
- Data Management (8 reviews)
- Features (8 reviews)

**Cons:**

- Expensive (16 reviews)
- Complexity (7 reviews)
- Difficult Learning (5 reviews)
- Poor Documentation (4 reviews)
- Access Issues (3 reviews)

### 6. [Discovery Hub](https://www.g2.com/products/discovery-hub/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:** 147
**How Do G2 Users Rate Discovery Hub?**

- **Data Integration:** 10.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.4/10 (Category avg: 8.7/10)

**Who Is the Company Behind Discovery Hub?**

- **Seller:** [Timextender](https://www.g2.com/sellers/timextender)
- **Company Website:** https://www.timextender.com
- **Year Founded:** 2006
- **HQ Location:** Aarhus, DK
- **Twitter:** @timextender (17,630 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/timextender/ (92 employees on LinkedIn®)

**Who Uses This Product?**
  - **Who Uses This:** Data Analyst, Business Intelligence Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 45% Mid-Market, 34% Small-Business


#### What Are Discovery Hub's Pros and 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)


    ## What Is Data Fabric Software?
  [IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure)
  ## What Software Categories Are Similar to Data Fabric Software?
    - [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)

  
---

## How Do You Choose the Right Data Fabric Software?

### 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.