The Forrester Wave™: Data Fabric Platforms, Q4 2025

Data Fabric Platforms Vendor Product Comparisons 2025

Key features and pros and cons of top Data Fabric Platforms products:

Cloudera - Leaders

Pros:

Unified platform integrates and orchestrates diverse data, delivering scalability and flexibility needed to drive actionable insights across the enterprise
Hybrid and multicloud portability ensures consistent data/workload management across complex environments at scale
End-to-end data fabric with native support of open table formats, unified data catalog, real-time performance, and advanced lakehouse features
Agentic AI capabilities bring intelligent automation and adaptive workload optimization to modern, distributed data environments
Strong metadata management, lineage tracking, and enterprise-grade security

Cons:

Need to accelerate innovation in agentic AI, automation, and intelligent data fabric capabilities according to roadmap

Microsoft - Leaders

Pros:

Unified, intelligent, and agentic data fabric that seamlessly integrates data management, advanced analytics, AI, and visualization at scale
Strong focus on innovation, advanced automation, open standards, enhanced self-service capabilities, and deeper integration across Microsoft Cloud ecosystem
Standout capabilities for end-to-end data fabric, agentic AI, distributed data processing, real-time data integration, metadata management, unified data catalog, multidomain support, and data products
Seamlessly integrates advanced data management, real-time processing, and translytical capabilities
Intelligence infused across the entire stack with features like copilots and digital twins for AI-powered apps

Cons:

Customers call for improved platform maturity
Need stronger integration with legacy systems
Greater transparency and control over long-term cost management needed

Oracle - Leaders

Pros:

Excels with advanced hybrid and multicloud data fabric deployments
Real-time data integration and transformation capabilities
Intelligent data modeling, unified access and search, data connectivity and streaming
High-performance distributed data processing enabling global scale via real-time data movement across clouds, data centers, and geographies
Powerful real-time data processing capabilities empower organizations to rapidly unlock deep insights from distributed data

Cons:

Need to accelerate innovation in AI-driven data capabilities, self-service-driven real-time transformation, autonomous agentic AI, and advanced data visualization according to roadmap

Snowflake - Leaders

Pros:

Distributed data processing and persistence capabilities
Multidomain data management and real-time data quality
Strong data connectivity and streaming capabilities
Secure, intuitive, LLM-powered assistant that simplifies data access
Growing AI capabilities enable automated anomaly detection, predictive analytics, and intelligent data transformation
Support for multicloud deployment, robust governance and security, advanced automation capabilities

Cons:

Need to continue execution on roadmap for enhanced interoperability, scalable distributed data processing, and intuitive self-service capabilities
Streamlining data pipelining and workload orchestration needed

Teradata - Leaders

Pros:

Proven performance and robust architecture ideal for powering mission-critical analytical and AI workloads in complex, distributed environments
Excels at operationalizing complex workloads via in-platform analytics, real-time data transformation, broad data integration, and advanced data modeling
Native connectors and prebuilt, industry-specific data models accelerate insights and simplify deployment in hybrid and multicloud environments
Flexible deployment options across on-premises, cloud, and hybrid environments
QueryGrid for federated querying, good scalability, and workload management

Cons:

Need to accelerate integration of AI and agentic AI capabilities
Real-time data quality enforcement and data sharing across ecosystems need enhancement
Expanded support for interactive data visualization needed

Denodo - Strong Performers

Pros:

Stands out for multidomain data management, real-time data integration, semantic layer, and unified data access and search
Seamless integration across diverse data sources
Strong metadata management and lineage capabilities
Flexible API and customer support
Broad partner ecosystem to support wider range of data fabric use cases

Cons:

Deploying large and complex data fabrics can be demanding due to limited availability of advanced automation
Challenges in maintaining data quality, particularly in real time
Room for improvement in self-service functionality, agentic AI, data modeling, documentation quality, and advanced automation features

Google - Strong Performers

Pros:

AI-native, serverless architecture that embeds advanced ML and analytics for high-performance, governed data platforms
Support for distributed transactions and self-service capabilities
Unified governance and real-time access to structured and unstructured data with Dataplex and BigQuery at the core
Serverless and deeply integrated cloud platform reduces operational overhead
AI and ML capabilities directly embedded into services

Cons:

Slower innovation in Dataplex has constrained ability to fully execute on data fabric ambitions
Limited multicloud flexibility
Evolving support for domain data management and data products constrains appeal for enterprises with heterogeneous cloud environments

Informatica - Strong Performers

Pros:

Superior support for structured data integration, governance, and real-time access across complex, distributed ecosystems
Metadata-driven platform that supports data quality, unified data catalog, governance, and AI-powered automation across hybrid and multicloud environments

Cons:

Falls short in delivering seamless hybrid and multicloud integration, robust semantic layers, distributed data processing, and intuitive self-service experiences
Heavy reliance on physical data movement through ETL and ELT limits ability to support modern, zero-copy logical data fabric
Need improvements in UI usability, high-end performance, agentic AI, advanced automation, integration flexibility, and cost transparency
Detailed product roadmap currently unclear due to Salesforce acquisition

InterSystems - Strong Performers

Pros:

Unified platform combines data integration, analytics, and transactional processing with high performance and real-time capabilities
Strong real-time, transactional workloads and embedded analytics
Good performance and real-time data access from a single platform
Ability to integrate and harmonize disparate data silos
Strong presence in healthcare, financial services, supply chain, and mission-critical systems

Cons:

Lags in comprehensive metadata management, agentic AI, and unified data catalog
Slower to roll out next-generation data fabric capabilities
Need more transparent roadmap and more advanced AI capabilities
Roadmap aims to address deeper native cloud integration, agentic AI, embedded data intelligence, and advanced automation

Qlik - Strong Performers

Pros:

Extensive data connectivity and real-time streaming across hybrid and multicloud
Seamless ingestion and movement of data from diverse sources into centralized, analytics-ready environment
Good support for building data products with end-to-end lineage and data quality features
Exceptional customer support, broad CDC replication, and extensive endpoint and transformation capabilities

Cons:

Consolidation of Qlik and Talend progressing but not yet fully realized
Reliance on physical data movement and replication can introduce operational complexity and higher infrastructure costs
Need for better monitoring, agentic AI, self-service, distributed data fabrics, and enhanced data lineage
More flexible cloud and API support needed

Amazon Web Services - Contenders

Pros:

Deeply integrated ecosystem enables seamless data movement, governance, and advanced analytics across diverse workloads within AWS environment
On par across most critical data fabric capabilities
Basic level of integration within AWS ecosystem
Reliable security features and fine-grained access controls
Simplified setup

Cons:

Vision for data fabric market is ambiguous with no clearly articulated strategy to unify sprawling portfolio
Data fabric approach remains fragmented, comprising suite of loosely coupled cloud services requiring deep expertise to integrate and operationalize
Absence of unified, logical data fabric layer
Still-maturing capabilities of next-generation SageMaker
Need to develop roadmap, strengthen genAI and LLM capabilities, expand intelligent data management, deepen multicloud support, and simplify integration and automation

IBM - Contenders

Pros:

Great support for real-time data transformation, lineage, and unified governance anchored by AI-driven Cloud Pak for Data platform
Governance and security features
Ability to integrate data across complex hybrid environments

Cons:

Vision has lost momentum in AI-driven era with limited clarity on unifying diverse product portfolio
Fragmented architecture built from suite of loosely integrated components adds significant complexity to deployment and ongoing operations
Drives up total cost of ownership and presents steep learning curve
Key gaps in MCP integration and real-time data quality
Need to simplify deployment processes, enhance real-time data integration capabilities, and offer advanced agentic AI and self-service features

Reltio - Contenders

Pros:

Advanced data modeling and strong capabilities for multidomain MDM
Cloud-native, real-time MDM foundation plus match/merge, data quality, and API-first architecture
Support for scalable integration, governance, and identity resolution
Real-time data delivery, flexible data modeling, APIs, and capabilities for ingestion, governance, and metadata

Cons:

Trails in broader data connectivity to unstructured data sources and IoT
Limited distributed data processing, agentic AI, and real-time transformation
Lacks native support for distributed transactions across federated environments
Excels in MDM but falls short on broader, end-to-end data fabric workloads
Need improvements in semantic modeling, documentation quality, and cost transparency

SAP - Contenders

Pros:

Data quality and semantic layer capabilities
Supports multidomain data models and embedded business process intelligence
Enhances data governance, quality, and real-time analytics
Strong integration with existing SAP systems
Ability to provide consistent, real-time data across complex enterprise environments

Cons:

Vision hindered by dependence on SAP-centric systems resulting in limited interoperability and flexibility
Lacks fully unified and intuitive data fabric experience, often requiring deep SAP expertise
Slower adoption of advanced technologies including AI, distributed data fabrics, and agentic AI
Need to accelerate support for non-SAP data sources and expand roadmap for distributed, multicloud data fabric deployments
Premium pricing model may deter potential buyers
Need better ease of use, faster deployment times, and enhanced support for non-SAP data sources

FAQs

Q: Which vendors are Leaders in 2025?

A: Cloudera, Microsoft, Oracle, Snowflake, Teradata

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave offer the strongest current product offerings and have the most robust strategies for the evaluated market. They demonstrate superior performance across current offering and strategy criteria, combining strong capabilities with a clear vision for innovation and market evolution. Leaders typically excel in end-to-end data fabric capabilities, advanced AI integration, scalability, and customer satisfaction.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for Data Fabric Platforms in 2025

Data Fabric Platforms Vendor Product Comparisons 2025

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FAQs

Q: Which vendors are Leaders in 2025?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

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