What is Lakeside AI?

AI apps require data. Lots of it. To succeed, organizations need data architecture that can get the job done. Without a foundation that can produce a steady stream of high-quality data quickly, AI initiatives sputter and stop before they ever take off. 

At Starburst, we’re frequently asked the same questions by businesses who are struggling to adapt to AI: 

• How can I access the right data and prepare it for AI?
• How can that data be queried quickly and cost-effectively to support high-performance AI workloads?
• How can I govern, trace, and audit my data that flows into AI, especially in regulated or high-risk environments?

Why enterprise AI is fundamentally a data problem

Legacy data platforms are not built for this moment. They’re sprawling, siloed, and brittle. While predictive models could once afford to operate downstream of the data, today’s generative AI demands something entirely different. It puts pressure upstream on how data is accessed, moved, shared, and governed.

Many companies are struggling to bring their data into AI. We have a better idea: Bring AI to your data. 

Let me explain. 

 

Why your AI workloads need a lakehouse 

AI systems interact with data differently than traditional analytics or data applications. Whether you’re asking an AI agent to return insights from enterprise data or fine-tuning a model to perform a domain-specific task using Retrieval Augmented Generation (RAG), success depends on fast, reliable access to high-quality data. Without it, AI results are incomplete, inaccurate, or delayed. And in most organizations, that data lives across dozens of siloed systems.

At the same time, your other data use cases haven’t disappeared. Your business needs a platform that can handle everything – from ingestion and ongoing analytics workloads to shareable data products and AI workflows, including AI agents and vector search.

Data foundations ready for the AI era 

Unfortunately, traditional data solutions—such as data warehouses and data lakes—can’t provide the scalability or security required by this wide variety of workloads. They’re too slow, too inflexible, and don’t provide the capabilities needed for cross-team collaboration and good governance. 

A better solution for AI is the data lakehouse. The lakehouse:

• Combines raw and structured data in one place. That means you don’t need to maintain separate lakes and warehouses.
• Scales like a data lake, but with warehouse-style performance and collaborative tooling. That enables large-scale querying that’s fast, reusable, and aligned across teams. 
• Supports access controls, versioning, and audit logs, allowing you to confidently track and manage your data.

Universal data access: Why a lakehouse alone isn’t enough

In theory, the data lakehouse solves the core challenges of access, performance, and governance. The problem is that not every organization can simply shift to a lakehouse overnight. 

Countless teams want to adopt the lakehouse model but can’t get there all at once. These are some of the most sophisticated organizations in the world: global enterprises, highly regulated industries, and innovative technology firms. Their data is siloed, sprawling, and often on-prem for real, strategic reasons.

This fractured environment makes it difficult, if not impossible, to leverage data for AI applications. Teams quickly find themselves blocked by multiple obstacles:

AI can’t learn from contextual data that it can’t reach

Data resides in scattered, slow-to-access systems that are not interoperable and are difficult to access.

Time to business value is slow

Without a central point of access for data, workflows are fragmented and require multiple hand-offs. Teams end up duplicating effort because they can’t find one another’s solutions. 

Siloed ownership and tooling have fractured governance for AI

Teams struggle because ownership is unclear. Compliance is overly complex. Data lineage, an indispensable feature for building trust in data, remains opaque and fuzzy. 

This is where Lakeside AI comes in. 

 

What is Lakeside AI? 

Most organizations can’t shift to a lakehouse overnight, but their AI initiatives can’t wait. Lakeside AI is a strategy for delivering AI-ready data today, without requiring a full data migration. Instead of forcing all data into a new architecture, Lakeside AI brings the lakehouse experience to your data, wherever it lives.

The Lakeside AI approach begins with federated access, enabling easy exploration and activation of data across your existing systems. From there, you can selectively pipeline high-demand datasets into a lakehouse format—but only when and where it adds value.

With Lakeside AI, organizations can:

• Access data across silos, without major refactoring
• Discover and explore data where it resides today
• Pipeline and optimize only the data needed for high-performance, governed AI workloads

Lakeside AI gives you a faster path to AI. It empowers you to generate insights, fine-tune models, and build AI agents using your real-world data. Most importantly, there’s no need to wait for a lengthy or complex migration.

 

Starburst brings your AI data lakeside

The Starburst data platform is built from the ground up to enable Lakeside AI. Starburst unifies data access and modernizes data architecture to give your organization a single point of access gateway for accessing all your distributed,  hybrid data today. 

Lakeside AI with Starburst  solves the challenges in accessing data for AI by providing: 

A single point of access

Using federated access, you can start today by exploring your data where it lives, powered by Trino for fast, scalable access across all your data sources. As your AI solutions mature, you can identify and move selected high-demand datasets into Iceberg for enhanced performance, security, and governance. 

A single point of collaboration

With Starburst, you can package and deploy datasets as data products, which boost AI workloads by providing reliability and trust. Using Lakeside AI, you can build end-to-end SQL workflows while keeping data secure. 

A single point of governance

By providing a single access point for data,  Starburst enables consistent policy enforcement across your data estate with federated governance, scalable compliance, and data audit trails. 

Using Starburst, you can bring the right data Lakeside. You can further leverage Starburst to build AI-ready tables with ease, embedding vectors, stream data, and bulk-loaded files directly into Iceberg tables. 

 

Why Lakeside AI belongs in your data stack

Lakeside AI is the fastest path for modern organizations that need to advance their AI initiatives. Instead of shifting all their data to a lakehouse, they bring AI to their data, wherever it lives.

Starburst brings the lakehouse experience to your data. It’s interoperable, hybrid, and built to enable seamless transition from data to AI, without delay.