Understanding Data Lakehouse Platforms for Modern Data and AI

Organizations are managing increasing volumes and varieties of data across separate systems for analytics, data science, and AI, often resulting in complexity, duplication, and operational inefficiencies.

In this Gartner® Market Guide for Data Lakehouse Platforms, explore how lakehouse architectures are designed to bring together capabilities traditionally associated with data lakes and data warehouses within a unified environment.

This research outlines key characteristics of data lakehouse platforms, including support for diverse data types, shared metadata and governance, and the ability to support multiple workloads from business intelligence to machine learning.

Access the report to gain an overview of the market, understand core capabilities, and evaluate considerations for adopting a lakehouse approach to data management.

“A lakehouse is a converged infrastructure design environment that combines the semantic flexibility of a data lake with the production optimization and delivery capabilities of a data warehouse. It supports the full progression of data from its raw, unrefined state, through the steps of refinement, to ultimately delivering optimized data for consumption.”

Source: Gartner Inc., Market Guide for Data Lakehouse Platforms, 2025

What You’ll Learn

• Key characteristics and capabilities of data lakehouse platforms
• How lakehouse architectures bring together data lake and data warehouse approaches
• Core features such as open table formats, shared metadata, and unified governance
• How lakehouse platforms support a range of workloads, from BI to AI and machine learning
• Considerations for evaluating lakehouse platforms in modern data environments

Why This Matters Now

Organizations are increasingly managing data across multiple systems, which can introduce complexity in data pipelines, governance, and operations.

Data lakehouse platforms are designed to support a unified approach to data storage, processing, and management, helping organizations reduce fragmentation and support diverse analytical and AI workloads within a single environment.

Who Should Read This Report

• Chief Data Officers (CDOs)
• Chief Information Officers (CIOs)
• Data and Analytics Leaders
• Data Platform and Architecture Teams
• Data Engineers and Data Architects
• AI and Machine Learning Leaders

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