The Forrester Wave™: Enterprise Data Fabric, Q1 2024

Enterprise Data Fabric Vendor Product Comparisons 2024

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

Denodo - Leaders

Pros:

Strong in deployment options, data access and search, data delivery, data processing and persistence, data transformation and lineage, and data integration
World-class data fabric solution with next-generation data management capabilities
Track record of growth with recent $336 million capital injection from TPG
Strong partner ecosystem supporting diverse data fabric use cases
Superior roadmap focusing on advanced data integration, genAI/LLM, vector search, faster data ingestion, automation, and self-service capabilities

Cons:

On par with others (not leading) in data models, data catalogs, data domains, data connectivity and streaming, data discovery and classification, data products, fabric transactions, and data quality

Informatica - Leaders

Pros:

Exceptional data fabric solution with strong vision focusing on automating, accelerating, and democratizing data fabric with genAI
Strong capabilities in performance, scale, data catalogs, data domains, data connectivity, streaming, data transformation and lineage, data discovery and classification, data processing and persistence, data quality, and data integration
Continues to make key investments through organic growth and acquisitions
Reference customers praise data quality, scalability, product vision, technical support, and data integration capabilities
Roadmap shows enhancements in process automation, knowledge graphs, generative AI/LLM, extensibility, ecosystems, data products, marketplace, and self-service capabilities

Cons:

Support for administration, data delivery, data products, data security, and end-to-end integrated solutions are on par with others evaluated (not differentiated)

Oracle - Leaders

Pros:

Excels in all enterprise data fabric use cases at scale
Key differentiator: support of globally distributed transactions spanning multiple data fabrics across geodistributed regions
Superior vision focusing on unified, intelligent, and highly automated data platform leveraging genAI for similarity searches, queries and pipeline automation, global transactions
Strong capabilities in performance and scale, data catalogs, data connectivity and streaming, data delivery, and fabric transactions
Reference customers praise geodistributed data management capabilities, technical support, interoperability, vision, and roadmap
Differentiated roadmap emphasizing distributed and multicloud architecture, integrated knowledge graphs, genAI/LLM, advanced data products, and self-service data integration

Cons:

On par with others (not leading) in end-to-end integration, deployment options, management, data models, data domains, data products, data access and search, and data quality

Teradata - Leaders

Pros:

Excels in data fabric with strong vision
Strategic strength in partner ecosystem essential for large and complex data fabric deployments
Clear, differentiated vision focusing on intelligent processing, democratizing data, accelerating time to value, empowering users, agile data onboarding, and semantic knowledge mapping
Strong in data discovery and classification, data access and search, and data processing and persistence
Superior roadmap focusing on genAI/LLM, process automation, data quality, data sharing/collaboration, metadata management, and UI personalization
Reference customers praise reliability, data portability, faster access to data, and cloud readiness

Cons:

On par with others (not differentiated) in performance and scale, deployment options, management, data models, data catalogs, data domains, data connectivity and streaming, data products, fabric transactions, data integration, and data quality

Cloudera - Strong Performers

Pros:

Strong in data processing and persistence
Vision has improved since last Forrester Wave evaluation on data fabric
Aggressively expanding data management capabilities for larger, more complex data fabric deployments
Roadmap promises more automation, genAI, advanced security and governance, advanced GPU integration, FinOps, and improved workload management
Reference customers like support for Apache Iceberg, hybrid approach, and LLM integration

Cons:

End-to-end data fabric is still evolving, especially around data integration, semantics, and modeling
On par with others for most criteria (not leading in most areas)

IBM - Strong Performers

Pros:

Strong in data models, data discovery and classification, data governance and security, and data quality
Longtime player in data management with comprehensive capabilities including master data management, data integration, data quality, data security, governance, and data lineage
Support for hybrid and multicloud environments
Roadmap focuses on knowledge graphs, semantics, genAI/LLM, governance, automation, and self-service

Cons:

Current position compromised due to inadequate data fabric marketing efforts, shifting business priorities, and lack of innovation
Lags in performance and scale, data products, fabric transactions, and data transformation
Declined to participate in full Forrester Wave evaluation process

InterSystems - Strong Performers

Pros:

Delivers high-performance data fabric supporting any use case at scale
Strong vision focusing on delivering integrated, consistent, trusted data at the right time
Strengths in performance, scale, and data delivery
Robust partner ecosystem
Reference customers like product support, scalability, flexibility, performance, and customer focus
Roadmap focuses on more integrated control plane, genAI/LLM, data intelligence, improved APIs, knowledge graphs, and advanced automation

Cons:

Needs better automation capabilities
Some references indicate admin tools are not intuitive, system takes time to learn, license costs are high
Lesser market presence can make it hard to find developers

Microsoft - Strong Performers

Pros:

Strong in data governance and security, and data access and search
End-to-end, unified analytics platform combining Azure Data Factory, Azure Synapse Analytics, and Power BI
Product looks competitive with leading fabric providers in coming years
Roadmap suggests improved integration of data management capabilities, genAI/LLM, broader data connectivity, and advanced automation

Cons:

Announced Fabric solution in May 2023, still not mature regarding data integration, semantics, and metadata management
Lags in performance and scale, deployment options, data catalogs, data domains, data delivery, and data quality
Declined to participate in full Forrester Wave evaluation process

Qlik - Strong Performers

Pros:

Stands out on deployment options, data connectivity and streaming, data delivery, data processing and persistence, and data integration
End-to-end, real-time data integration and analytics platform across hybrid cloud
Recent acquisition of Talend positions combined entity for potential market disruption
Reference customers praise range of connectivity, monitoring and operations, and technical support
Roadmap extends capabilities on genAI/LLM, integrated semantics, persona-based UI, automation, and self-service

Cons:

Product integration with Talend likely to take time given complex portfolio, may impact pace of innovation
Some references express concerns that Qlik-Talend merger may create uncertainty and slow innovation
Innovation is lagging

SAP - Strong Performers

Pros:

Stands out on data models, data domains, data discovery and classification, data governance and security, data access and search, data transformation and lineage, and data quality
Makes data orchestration and quality easy
Strong partner ecosystem covering needs of most customers
Reference customers like easy sharing of data insights, connectivity to various systems, technical support, and maintenance carried out by SAP team

Cons:

Lags in end-to-end integration
Lags other vendors on innovation, adding capabilities that are important but not groundbreaking
Some customers concerned about self-service capabilities and incremental improvements that often disrupt business operations

Google - Contenders

Pros:

Excellent data governance and security capabilities
Broad data fabric offering across operational and analytical sources
Focuses on common foundational platform running across multiple clouds, including on-premises
Reference customers like ease of use, technical support, and AI capabilities
Roadmap focused on genAI/LLM, data modeling, vector search, integration with lakehouses, data collaboration, metadata, advanced security, and discovery

Cons:

Has viable analytical data fabric solution but lags in semantics and automation
Lacks cutting-edge data fabric capabilities to differentiate from competition
Lags in end-to-end data fabric integration and innovation
Some customers concerned about comprehensiveness of solution, time, effort, and expense associated with deployment

Hewlett Packard Enterprise - Contenders

Pros:

Good data processing capabilities
On par with other vendors on performance and scale, deployment options, management, data access and search, data processing, and persistence capabilities
Reference customers like portability, support, accessibility, and flexibility
Competitive product vision

Cons:

Lags in core data fabric capabilities
Many features behind competition, including data models, data catalog, data domains, data connectivity and streaming, data discovery and classification, data governance, data delivery, data products, data integration, and fabric transactions
Lags in overall data fabric strategy with little demonstrated ability to make key investments in cutting-edge innovation
Some customers concerned about lifecycle management, product vision, lack of support from open-source community, and upgrade paths

K2view - Contenders

Pros:

Cost-attractive data fabric solution
On par with other vendors on performance and scale, deployment options, data connectivity and streaming, data discovery and classification, data transformation and lineage, data catalogs, data governance and security, data delivery, data products, data processing and persistence, fabric transactions, data integration, and data quality
Reference customers like enabling of customer 360 and existing business processes with real-time data
Roadmap focuses on data observability, scalability, FinOps, DataOps, genAI/LLM, and automation

Cons:

Lags in comprehensive capabilities
Many enterprise features trail others evaluated, including data models, data domains, data access, search, and management
Some customers concerned that solution is complex and laborious to operate, with high dependency on vendor expertise
Initial data load reported as high effort

Solix Technologies - Contenders

Pros:

Foundational fabric with low-cost data fabric solution
Scores mainly on par in several areas: performance and scale, deployment options, management, data transformation and lineage, data discovery and classification, data access and search, data delivery, data processing and persistence, data catalog, data integration, and data quality
Making progress with more integrated experience and improved performance, scale, and data experience
Reference customers like vendor support, responsiveness, breadth of connectivity, cost, and data validation
Roadmap suggests leveraging genAI, advanced process automation, improved lineage, more connectors, data products, data domains, cloud-native scalability, and fabric transactions

Cons:

Lags in advanced capabilities to compete head-on with large data fabric vendors
Lags in data models, data domains, data connectivity and streaming, data products, and fabric transactions
Some customers say documentation is limited, system upgrades are painful and time-consuming, data ingestion tools are basic, and product roadmap appears weak

TIBCO Software - Contenders

Pros:

Viable solution with unified solution for real-time data integration and management
Scores well in data catalog, data domains, data connectivity, and data integration
Good foundational data fabric components crucial for deployments
Roadmap on par with other vendors, focused on creating unified data platform bringing together capabilities across data management, data virtualization, integration, event processing, and messaging

Cons:

Lags in end-to-end integration and automation
Lacks comprehensive capabilities for large and complex use cases
Lags on integrated capabilities and automation to accelerate deployment
Customers often require consulting services to help with integrating various data fabric components
Lags in management, data products, fabric transactions, data transformation and lineage, and end-to-end data fabric solutions
Lags in innovation and vision
Declined to participate in full Forrester Wave evaluation process

FAQs

Q: Which vendors are Leaders in 2024?

A: Denodo, Informatica, Oracle, Teradata

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

A: Leaders are vendors that excel in enterprise data fabric use cases at scale, with superior vision focusing on unified, intelligent, and highly automated data platforms. They demonstrate strong capabilities across performance, scale, data management, integration, and have differentiated roadmaps emphasizing innovation in areas such as distributed architecture, genAI/LLM, knowledge graphs, and advanced data products.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for Enterprise Data Fabric in 2024

Enterprise Data Fabric Vendor Product Comparisons 2024

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FAQs

Q: Which vendors are Leaders in 2024?

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

Q: How do I interpret a Forrester Wave?

Reference