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The Current State of Data Architecture
Understand how traditional data warehouses and data lakes are evolving — and why many organizations struggle with complexity, redundancy, and rising costs in fragmented data environments.
The Data Lakehouse Defined
Learn what a data lakehouse is and how it unifies the best of both data lakes and warehouses — combining scalability, flexibility, and performance in a single architecture built for AI and analytics.
Components of the Architecture
Explore the key elements of a lakehouse architecture, including open formats, metadata layers, governance, query engines, and multi-cloud compatibility — all designed to support modern workloads.
Cost Optimization Opportunities
Discover how a lakehouse reduces storage duplication, simplifies operations, and cuts licensing costs — enabling you to manage more data at a lower total cost of ownership.
Analytics and Data Science Enhancements
See how lakehouses accelerate analytics and AI by supporting real-time queries, direct access to raw data, and seamless integration with machine learning tools.
IBM watsonx.data
Learn how IBM watsonx.data brings the lakehouse architecture to life — offering an open, governed, and performant platform for managing structured and unstructured data across your enterprise
2025