Starburst
Turning data into AI-ready insights

What if a company could pull data from decades-old Hadoop systems and cutting-edge Iceberg tables into a single, real-time dashboard? Or ingest data into Iceberg at blistering speeds of up to 100 GB per second?
That’s exactly what Starburst is making possible—and in doing so, it's redefining what a data lakehouse can be.

“We’ve effectively blurred the lines between a data warehouse and a data lake,” says Matt Fuller, Starburst co-founder and VP of Product. “It’s no longer about choosing between speed and scale—you can have both.”

Performance at scale with Trino and Iceberg

At the core of Starburst’s breakthrough is its Icehouse architecture. This architecture pairs Trino—a powerful distributed query engine—with Apache Iceberg, an open table format purpose-built for massive datasets.

Fuller puts it this way: “We built the platform around Trino, but that’s just the engine. We had to add the rest of the car—the security, the governance, the user experience. That’s what makes Starburst a platform, not just a query engine.”

We’ve effectively blurred the lines between a data warehouse and a data lake. It’s no longer about choosing between speed and scale—you can have both

A new era of native indexing

Another standout innovation from Starburst is Warp Speed, the first indexing and caching solution built specifically for data lakehouses. Traditional lake architectures often require scanning massive files, which slows down query times. Warp Speed solves that by preloading frequently accessed data and building indexes optimized for specific filters and joins.

Fuller explains, “Warp Speed addresses the biggest knock against data lakes—that they’re slow. Now, you get the flexibility of a lake with the speed of a warehouse.”

By bringing indexing directly to the data lakehouse, Starburst significantly improves query performance without requiring a complete overhaul of existing systems.

Starburst offers two deployment options tailored to different enterprise needs. Starburst Galaxy is a fully managed, cloud-based SaaS solution built for organizations that need rapid setup and cloud-native scalability. The Starburst Enterprise Platform is designed for companies that require on-premises control and air-gapped security.

In both cases, Starburst makes it easy for data teams to ingest raw data from sources like Kafka topics or file systems, convert that data into Iceberg tables, and apply quality checks and transformation logic. The result is refined, trustworthy data that can serve a wide range of applications—from BI dashboards to advanced AI tools. This can even lead to the creation of data products–curated datasets that help improve access, collaboration, and governance.

Starburst also scales automatically to meet changing demands. For instance, a Starburst cluster might expand to 30 nodes during peak usage and contract to 10 during off-hours, ensuring efficient cost management. Larger enterprises can deploy multiple clusters across departments and allocate costs using show-back and charge-back capabilities.

Real-world impact: From complexity to clarity

There are many examples of Starburst in action. One global media company was struggling with deeply siloed data stored in legacy Hadoop systems. Reporting was slow and unreliable, and efforts to modernize had stalled due to the sheer size and complexity of the data. By implementing Starburst, they were able to federate queries across both Hadoop and modern Iceberg tables. This gave them the ability to deliver unified dashboards that updated in near real-time—all without migrating terabytes of legacy data.

Another customer faced a different challenge. They were building large language model (LLM) capabilities but found that raw, uncurated data produced generic or inaccurate results. By leveraging Starburst’s transformation and metadata management features, they were able to create high-quality, business-context-rich data products.

These improvements enhanced the relevance of AI-generated responses and gave business teams greater confidence in using AI-driven tools.

Open standards and enterprise-grade control

Security, compatibility, and governance are foundational to the Starburst platform. It supports a range of authentication methods, including SSO and Okta, and offers both role-based and attribute-based access controls. Organizations can define access rules down to the row and column level, ensuring data is shared securely and appropriately—without unnecessary duplication.

Starburst also avoids vendor lock-in by supporting a wide range of formats, including Iceberg, Delta Lake, Apache Hudi, and legacy Hadoop formats. This enables organizations to modernize their infrastructure at their own pace while maintaining full interoperability.

With Starburst, every dataset—regardless of where it resides—can be accessed and governed consistently, helping organizations simplify policy management and streamline analytics across the board.

Automation, AI, and the future of the lakehouse

Starburst is not just focused on solving today’s challenges—it’s actively building the future of data infrastructure, one where intelligence and automation drive the process from ingestion to insight.

“We’re building toward a future where data ingestion and transformation are as simple as flipping a switch,” says Fuller. “That way, engineers can focus on delivering insights, not plumbing.”

As organizations integrate more AI into their data strategies, platforms like Starburst will play a pivotal role. By streamlining data preparation, improving access control, and enabling real-time performance, Starburst is making advanced analytics and AI more accessible and impactful than ever before.

In a space long defined by overpromises and under delivery, Starburst is delivering something rare: a lakehouse platform that actually works—fast, flexible, and future-ready.