How do data lakehouses and data federation work together? They may seem unrelated, but actually, the two technologies operate in tandem to address some of the largest data architecture challenges facing organizations. Together, they create a convergent data strategy that greatly enhances data access and governance, moving beyond what was achievable with earlier data warehouses or data lakes.
Let’s explore how data federation and data lakehouses have converged into a single data architectural paradigm, and how that strategic approach is upending the value of data and driving analytics and AI workloads.
Setting out the problem that data federation and data lakehouses solve
Enterprise data no longer lives in one place. Because of this, architecture built on an assumption of data centralization is breaking down.
In fact, most organizations today operate with a plurality of technologies and strategies. That means multiple cloud environments, cloud regions, on-premises environments, and internal business units, each with distinct, heterogeneous systems and governance rules.
In this context, moving all your data into a single data warehouse simply won’t work. Not only does it introduce slower pipelines, but it also increases costs and introduces regulatory and compliance friction.
The role of data federation
Federation offers an alternative. Instead of moving data, it lets teams access, query, and govern it across any data source without movement. At the same time, federation isn’t an either-or proposition. You can federate some data and move other data when it makes sense. Having this choice matters as scale increases and as AI workloads depend on timely, governed access to diverse datasets.
The role of data lakehouses
The modern data lakehouse supports this approach as well. The data lakehouse provides the storage, metadata, and governance foundation that makes federation practical at enterprise scale. In this, the data lakehouse blends the flexibility of a data lake with the reliability and performance of a warehouse, and it provides the core capabilities required for federated access across analytics and AI.
Open table formats make the data lakehouse function
Technologies like Apache Iceberg provide enhanced support for transactional data, enhanced metadata usage, and a stronger governance layer that supports features like schema evolution and time travel.
When paired with data federation, these features become even more powerful, adding universal data access alongside strong transactional support. The result is a winning combination of access and capability that helps drive forward data workloads of any type.
Separation of storage and compute
Traditional architectures tightly couple where data is stored with how it’s processed. The lakehouse separates these concerns, allowing compute resources to scale independently and enabling queries to run against data wherever it lives.
This separation works in parallel with data federation. Teams can provision and scale compute resources independently, access data from any data source, and choose the architecture that best fits their performance, cost, and governance requirements.
Unified governance layer
The lakehouse provides centralized policy enforcement that travels with the data. Row-level security, column masking, and access controls can be defined once and applied consistently across all data, regardless of where it physically resides.
Domain teams gain autonomy to manage their data products while the enterprise maintains oversight, security, and compliance. This balance makes federated architectures work at scale.
Performance at scale
Federated queries are only effective if the underlying architecture can sustain performance across distributed systems. Modern data lakehouses include several features that enable this.
- Query engines push computation down to the source systems, filtering and aggregating data as close to its origin as possible.
- Cost-based optimizers and statistics guide efficient join and scan planning across heterogeneous sources.
- Metadata caching and data skipping reduce unnecessary reads, while adaptive query planning accounts for network latency and data locality.
Together, these capabilities minimize data movement, improve parallelism, and keep federated query performance predictable as workloads scale.
The AI imperative for federation
AI has become the defining pressure test for every data architecture. Models can’t train effectively on incomplete data, yet centralizing all training data is often impractical or impossible due to scale, cost, or regulatory constraints.
Federated data architecture powered by a lakehouse enables AI teams to access training data from multiple domains without migration, maintain strict governance over sensitive data used in models, support real-time inference that queries operational systems directly, and deploy enhanced data version control for model reproducibility.
Lakeside AI
This is where the concept of Lakeside AI emerges. It describes a data strategy where a lakehouse architecture is designed to support both traditional analytics and AI workloads through federated access, collaborative data products, and open standards.
Making federation work: The Icehouse architecture
The time has come for the union of data federation and the data lakehouse. Many platforms marketed as lakehouses still assume centralization or introduce proprietary elements that undermine true federation.
Starburst Icehouse Architecture represents an approach built for federated use cases. It combines:
Trino
Trino is an open-source query engine designed for federated queries across distributed data sources.
Apache Iceberg
The open data lakehouse table format provides the reliability and governance needed for enterprise federation.
Hybrid flexibility
True multi-cloud, on-premises, and edge support means data can stay where regulations, latency, or business logic require while remaining accessible through a unified query layer.
This combination delivers federation without compromise. You don’t sacrifice performance, governance, or openness to query data anywhere.
Want to know more about data federation and data lakehouses? Check out this eBook, The Iceberg Data Lakehouse, for more information.
