The Forrester Wave™: Data Management For Analytics Platforms, Q2 2025

Data Management For Analytics Platforms Vendor Product Comparisons 2025

Key features and pros and cons of top Data Management For Analytics Platforms products:

Databricks - Leaders

Pros:

Excels in data persistence using an open table format
Strong capabilities in data pipelines, real-time optimization, and data security
High-end performance and scale
Empowers team collaboration with shared notebooks and integrated ML flow
Commitment to open source and open standards

Cons:

Can improve data quality and data integration by supporting real-time processing
Customers desire improvements in data visualization and data quality management
Need for better visibility into timeline for new features
Simplification of governance capabilities needed

Google - Leaders

Pros:

Distinctive and forward-thinking vision for unified, agentic, intelligent platform
Exceptionally well-defined roadmap with AI positioned at its core
Stands out in data and AI capabilities with Gemini and agent-powered analytics
Planet-scale computing and real-time data processing capabilities
Robust suite of data visualization tools including Looker
High customer satisfaction with performance, scalability, security, and real-time processing

Cons:

Customers express desire for more automation features in data integration from disparate source systems
Need for enhanced operational monitoring tools

Oracle - Leaders

Pros:

Strong vision with commitment to expanding DMA use cases through continuous innovation
Superior DMA roadmap emphasizing AI assistants, genAI/LLM automation, and seamless integration
Excels in hybrid and multicloud deployments
Strong capabilities in real-time data movement across repositories, data centers, and regions
Robust partner ecosystem and outstanding technical support
Customer favorite in this evaluation

Cons:

Opportunities to enhance support for genAI/LLM and automation
Could improve data modeling, transformation, governance, and data visualization

Snowflake - Leaders

Pros:

Pioneer of separating compute and storage in data platforms
Superior roadmap emphasizing advanced interoperability and self-service tools
Automatically scales compute resources in real time based on demand
Seamlessly automates key management functions like data partitioning, indexing, and query optimization
Intuitive, secure, LLM-powered assistant that streamlines data analysis
Customers praise simplification of data management and data-sharing capabilities

Cons:

Vision is on par rather than leading edge
Need to continue expanding beyond core data warehouse capabilities to broader use cases

Teradata - Leaders

Pros:

Strong and differentiated vision to democratize access to data and AI
Excels in seamless operationalization of workloads with in-platform analytics
Strong data modeling, transformation, and data integration capabilities
Cutting-edge tools, extensive data connectors, and prebuilt industry-specific data models
Flexible deployment options including on-premises, cloud, and hybrid environments
Customers appreciate real-time capabilities, data security, and data pipelining

Cons:

Customers desire improvements in more advanced data visualization
Need for more integrated multicloud management and simplified genAI capabilities
High total cost of ownership mentioned by customers

Amazon Web Services - Strong Performers

Pros:

Broad data and analytics capabilities that are highly customizable
Stands out with data transformation, data governance and performance DMA capabilities
Next generation of Amazon SageMaker unifies data, analytics, and AI tools into single platform
Strong innovation strategy with focus on genAI, real-time data management, and integration
Strong partner ecosystem and competitive pricing

Cons:

Vision for data management in analytics remains a work in progress
Need for improvement in data modeling, data quality, and ease of use for core data management functions
Desire for more consistent product experience and focus on delivering on roadmap

Cloudera - Strong Performers

Pros:

Excels in data persistence by supporting open table formats like Apache Hudi, Apache Iceberg, and Delta Lake
Consistently scores on par across DMA capabilities
Strong emphasis on open-source technologies
Customers appreciate integrated data management capabilities and maturity of data catalog and metadata management
Hybrid, flexible platform supporting wide range of data-related use cases

Cons:

Vision and innovation are on par rather than leading
Roadmap is tactical and focused on addressing platform gaps that competitors already support
Must accelerate genAI/LLM strategy to stay competitive
Customers desire better developer experience for DMA and more advanced automation

IBM - Strong Performers

Pros:

Excels in providing diverse deployment options and in-platform analytics
Strong data quality management, data access, and governance features
Robust capabilities for maintaining data integrity, security, and compliance
Data governance and quality received high rating due to mature, industry-leading features
Seamless data integration capabilities

Cons:

Vision and roadmap are on par with other vendors rather than leading
Areas where IBM could enhance offerings include advanced AI capabilities, automation, and data visualization tools
Automation and visualization received lower customer ratings

Informatica - Strong Performers

Pros:

Excels in data governance, data quality, and data transformation
Broad suite of data management and integration solutions
Strong metadata management capabilities
Helps organizations ensure accuracy, compliance, and value across the enterprise

Cons:

Falls short in managing diverse data types in real time
Lacks integrated data intelligence across repositories
Missing AI-enabled data pipelines and cohesive data persistence
Customers note it falls short in ease-of-use and self-service features
Lags in advanced DMA AI capabilities, unstructured data management, and comprehensive visualization tools

Microsoft - Contenders

Pros:

Excels in data visualization through robust Power BI offering
Customers praised expertise, support, and visualization offerings
All-encompassing DMA solution that integrates data engineering, data science, warehousing, and BI
Streamlines entire data lifecycle from ingestion to analysis

Cons:

Trails behind in vision and roadmap for DMA offerings
Underlying data infrastructure for Microsoft Fabric requires enhancement and further development
Falls short in automation, data pipeline orchestration, semantic data modeling, intelligent data integration, data quality, and integrated governance
Organizations often must rely on systems integrators to implement and manage fully integrated DMA environment
Customers highlighted areas for improvement in data integration, data quality, data modeling, performance, scalability, and streaming data

SAP - Contenders

Pros:

Suite of data management solutions including SAP HANA, SAP Datasphere, and SAP Analytics Cloud
Well-suited for large organizations already using other SAP products
Offers seamless integration and enhanced data management capabilities for SAP customers
Broad data management capabilities

Cons:

Vision for the DMA market lacks clarity
Unclear how SAP intends to align diverse product portfolio and integrate with AI capabilities
Most DMA capabilities lag behind others evaluated
Lacks fully integrated and seamless DMA experience
Key areas such as real-time data processing, quality, and modeling are complex and require significant customization
Slower in adopting advanced technologies like AI and machine learning
Customers note it lacks fully integrated DMA solution, often requiring systems integrators
Lags in incorporating advanced AI capabilities within data management offerings

FAQs

Q: Which vendors are Leaders in 2025?

A: Databricks, Google, Oracle, Snowflake, Teradata

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

A: Leaders in the Forrester Wave demonstrate strong current offerings with comprehensive data management for analytics capabilities, and they exhibit robust strategies for future market evolution. Leaders excel in areas such as AI-driven automation, built-in data intelligence, support for diverse data types at scale, real-time processing, and integrated platform capabilities. They typically receive high customer satisfaction ratings and show clear vision and roadmap for advancing DMA capabilities including genAI/LLM integration, automation, and intelligent data management. Leaders offer solutions that can support both short-term and long-term data strategies for enterprises.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for Data Management For Analytics Platforms in 2025

Data Management For Analytics 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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