Navigating the Shift to a Lakehouse Architecture

The move to a lakehouse architecture represents a significant step forward in modernizing data platforms, eliminating inefficiencies, and preparing for future innovations in data management. However, as organizations embark on this journey, they must align expectations with the evolving capabilities of the lakehouse model.

Insights from Industry Research

A recent industry report highlights key considerations for organizations adopting lakehouse architectures:

A Lakehouse is a Step Forward, Not an Endpoint
“Approaching lakehouse architecture as an endpoint in design is more likely to fail, whether during the move or in the future. It is not an endpoint in design but a step on the way to the next major transformations: the data fabric and data ecosystem.”*
Setting the Right Expectations
While the lakehouse model blends capabilities from both data lakes and warehouses, it remains an evolving solution. Many organizations anticipate seamless data access and transformation, but success requires careful planning and an understanding of lakehouse maturity.
Reducing Redundancy and Complexity
“The primary reason for moving to the lakehouse architectural pattern should be to reduce the redundancy of lake-to-warehouse integration… Redundancy increases the complexity and maintenance of the integrated platforms, slows data movement, and challenges governance, standards, and optimization.”*

*Source: Gartner, Follow These Best Practices to Migrate to the Lakehouse, Henry Cook, Thornton Craig, Prasad Pore, Ehtisham Zaidi. TOPICS, 6 January 2025

Take the Next Step in Your Data Journey

Dremio enables organizations to implement an open and scalable lakehouse architecture designed for performance, governance, and AI-ready data. Learn how organizations are optimizing their data environments by accessing this industry research.

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