Report:

A: This research covers a comprehensive 24-criterion evaluation of data lakehouse providers, assessing their current offerings, strategy, and market presence. It evaluates 13 major vendors across capabilities including data storage and formats, data connectivity, data ingestion/pipeline, data models, security and governance, data processing, genAI/LLM features, performance optimization, and end-to-end integration. The evaluation categorizes vendors as Leaders, Strong Performers, Contenders, and Challengers based on their strengths in delivering integrated solutions, genAI capabilities, and performance at scale.

A: This research should be used by data management professionals, data engineers, architects, scientists, business analysts, and IT decision-makers who are evaluating and selecting data lakehouse providers for their organizations. It is particularly valuable for organizations looking to migrate from traditional data warehouses and data lakes to modern lakehouse architectures, those seeking to support use cases like business intelligence, data science, AI/ML, real-time analytics, customer 360, and IoT insights, and enterprises planning to reduce costs, improve data governance, and accelerate time to value for new business initiatives across hybrid and multi-cloud environments.

A: To be included in this Forrester Wave evaluation, vendors must provide: (1) Enterprise data lakehouse capabilities as described in The Data Lakehouse Landscape, Q4 2023, including in-lakehouse analytics, data quality, automated management, data persistence, data integration, scale-out optimization, BI optimization, data science optimization, generative AI, data connectivity, data pipeline, data governance, data security, and data resilience; (2) Support for a broad set of use cases with customers using the solution in scaled production environments for real-time analytics, business intelligence, data science (AI/ML), data warehouses, data lakes, data integration, data sharing/collaboration, customer 360, IoT analytics, and/or data exploration; (3) Forrester mindshare demonstrated through mentions in Forrester enterprise client interaction calls related to data lakehouse topics during the past 12 months.

A:

• Lack of comprehensive enterprise data lakehouse capabilities
• No customers using the solution in scaled production environments for the required use cases
• Insufficient Forrester mindshare or mentions in enterprise client interactions
• Point solutions that only address specific aspects rather than end-to-end lakehouse functionality
• Solutions focused only on data warehousing or data lakes without lakehouse convergence capabilities
• Vendors declining to participate in the evaluation process (noted as nonparticipating)
• Partial participation where vendors did not provide complete information for evaluation

A:

• Leverage genAI capabilities in the platform - look for natural language query, code generation, vector capabilities, data intelligence, and LLM integration with foundational capabilities validated by customer references
• Deliver an end-to-end integrated lakehouse experience - seek vendors offering single pane of glass management for multiple distributed lakehouses with persona-based UI and integrated governance
• Deliver performance at the speed of business - prioritize solutions with deep integration with table formats (Apache Iceberg, Hudi, Delta), built-in automated performance optimization, advanced workload management, and parallel data processing and transformation
• Evaluate vendor's vision and innovation capabilities for future market evolution
• Assess roadmap alignment with your organization's use cases and requirements
• Consider partner ecosystem strength for complex deployments
• Review deployment options (cloud, hybrid, multicloud) matching your infrastructure strategy
• Validate security and governance capabilities meet your compliance requirements

A:

• Organizations migrating data lakes and data warehouses to lakehouses to reduce costs, improve data governance, and support real-time insights
• Broad growth in data lakehouse initiatives across all industries including financial services, retail, healthcare, manufacturing, and energy
• Integration of genAI capabilities including natural language query, code generation, vector capabilities for similarity searches, data intelligence, and LLM integration
• Demand for end-to-end integrated lakehouse experiences with streaming, transformation, workload management, integration, governance, and security
• Organizations leveraging data lakehouses for multiple use cases simultaneously
• Focus on performance at the speed of business with provisioning in minutes versus months for traditional systems
• Deep integration with open table formats such as Apache Iceberg, Hudi, and Delta

A: Strength of Current Offering (50% weighting) evaluates the vendor's existing product capabilities across 16 technical criteria including data storage, connectivity, processing, transformation, security, governance, genAI/LLM capabilities, performance, scalability, and deployment options. Strength of Strategy (50% weighting) assesses the vendor's future direction and market approach across 6 criteria: vision for the market, innovation capabilities, product roadmap, partner ecosystem strength, customer adoption, and pricing models. Current Offering focuses on what the vendor can deliver today, while Strategy focuses on the vendor's ability to meet future market demands and their competitive positioning.