The Forrester Wave™: Cloud Data Pipelines, Q4 2023

Cloud Data Pipelines Vendor Product Comparisons 2023

Key features and pros and cons of top Cloud Data Pipelines products:

Confluent - Leaders

Pros:

Data delivery
Observability
Data ingestion
Data access
Data lineage
Deployment options
Performance and scale
Multicloud strategy
Integrated experience
Open protocol
Technical support
Platform stability

Cons:

Metadata and data modeling are weaknesses

Databricks - Leaders

Pros:

Generative AI
Observability
Data ingestion
UI and automation
Data transformation
Performance and scale
Technical support
Fast implementation
Central data and AI governance
Open standards

Cons:

Informatica - Leaders

Pros:

Self-service
Automation
Performance and scale
Data ingestion
Data delivery
Data integration
Metadata and data models
Data transformation
Data access
Data quality
Data lineage
Customer support
Completeness of vision
Operational cost
Ease of use

Cons:

Reliability and resilience is a weakness

Microsoft - Leaders

Pros:

Observability
UI and automation
Data integration
Data transformation
Generative AI
Data access
Self-service
Security
Quality and governance
Ease of use
Real-time data processing
Unified customer view
Resilience
AI capabilities

Cons:

Data delivery is a weakness
Innovation for standalone Azure Data Factory has taken a back seat due to Microsoft Fabric focus

Oracle - Leaders

Pros:

Data access
Deployment options
Data delivery
Performance and scale
360 vision
Support for distributed geolocation data
AI/ML integration
Interoperability
Low-code integration
Real-time analytics capabilities

Cons:

CData Software - Strong Performers

Pros:

Data sources
Data access
Many data connections
Multicloud support
Multiple streams to meet client's needs
Customer support

Cons:

Lags in automation
Data ingestion
Generative AI
Data security and governance capabilities
Lagging with comprehensive API support
No streamlined way to upgrade

Fivetran - Strong Performers

Pros:

Data transformation
Data quality
Customer support
Ease of operation
Price/performance
Automation
Support for various data sources

Cons:

Lags in adding generative AI features
Costs for large deployments
Metadata management
Lack of mainframe sources
Limited data destinations
Extensibility

Google - Strong Performers

Pros:

Broad range of CDP capabilities

Cons:

Lags in semantics
Lags in high-end data ingestion and integration
CDP roadmap has taken a back seat
No areas where product stands out from others evaluated

Matillion - Strong Performers

Pros:

Data ingestion
Data integration

Cons:

Lags in data quality and automation
Metadata and data models
Generative AI
Data security and governance
Hasn't demonstrated innovation in CDP space
Playing catch-up with leading CDP vendors

Amazon Web Services - Contenders

Pros:

Cons:

Lacks automation and multicloud support
Data sources
Data delivery
Self-service
Automation
Extensibility
Metadata and data models
Deployment options
Performance and scale

Ascend.io - Contenders

Pros:

Data delivery
Data transformation
Automated orchestration
Infrastructure management
Customer support
Autoscaling

Cons:

Lags in innovation
Self-service
Extensibility
Metadata and data models
Generative AI
Data security and governance
Deployment options
Cost
Lack of real-time capabilities
Table-by-table ingestion
Need for technical expertise to use the platform

Qlik - Contenders

Pros:

Data sources
Extensibility of the platform

Cons:

Lags in innovation and roadmap
Generative AI
Data quality
Roadmap unclear after Talend acquisition
Cloud resilience
Data intelligence
Automation

SnapLogic - Contenders

Pros:

Broad set of connectors

Cons:

Lags in broad CDP capabilities
CDP automation
Extensibility
Data ingestion
Data integration
Metadata and models
Data transformation
Data quality
Data security and governance
Performance and scale

Software AG - Contenders

Pros:

Cons:

Lags in generative AI
Observability
Data integration
Metadata and data models
Data security and governance
Deployment options
Performance and scale
Error handling and logging
Price increases
Data quality capabilities
Observability tools
Lack of support for legacy protocols
Lack of native cloud

Hitachi Vantara - Challengers

Pros:

Cons:

Lags in enterprise capabilities
Data sources
Observability
Extensibility
Data integration
Metadata and data models
GenAI
Data access
Data security and governance
Data quality
Performance
Partner ecosystem is underdeveloped
Strategy is light on innovation
Doesn't have proven record showing ability to execute

FAQs

Q: Which vendors are Leaders in 2023?

A: Confluent, Databricks, Informatica, Microsoft, Oracle

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

A: Leaders in Forrester Wave evaluations are vendors that have the strongest current offerings and strategies in the market. They demonstrate strong vision, innovation capabilities, and robust roadmaps. Leaders typically excel across multiple evaluation criteria including current offering capabilities, strategic vision, innovation, partner ecosystem, and market adoption. In this Cloud Data Pipelines evaluation, Leaders show exceptional performance in areas such as deployment capabilities, data processing, automation, integration, security, governance, and ability to support enterprise-scale use cases across hybrid and multicloud environments.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for Cloud Data Pipelines in 2023

Cloud Data Pipelines Vendor Product Comparisons 2023

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FAQs

Q: Which vendors are Leaders in 2023?

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

Q: How do I interpret a Forrester Wave?

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