Overview
Starburst Galaxy is a fully managed data lake analytics platform designed for large and complex data sets in and around your cloud data lake. It is the easiest and fastest way for you to start running queries at interactive speeds across data sources using the business intelligence and analytics tools you already know.
Starburst Galaxy takes just minutes to set up and takes care of the heavy lifting of designing, provisioning, maintaining, and securing your Trino infrastructure. In addition, Galaxy offers proprietary features such as fully managed connectors, global search, schema discovery, monitoring and metrics, and data sharing with data products that allow your data teams to focus on generating unique insights from your data - not managing and building analytics infrastructure.
Highlights
- Simplicity - Starburst Galaxy lets you discover, govern, and prepare your data from a single, fully-managed platform. Future-proof your architecture with a single point of access and governance to all your data, including RBAC and ABAC capabilities.
- Scalability - Built on top of a query engine designed to run at internet-scale, Starburst Galaxy automatically scales your infrastructure to the needs of your workload in just a few clicks.
- Optionality - Starburst Galaxy works with any data storage and table format, so you never have to worry about locking yourself into a proprietary data ecosystem.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Buyer guide

Financing for AWS Marketplace purchases
Pricing
Vendor refund policy
No refunds.
Custom pricing options
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Get help directly from Starburst in the Starburst Galaxy UI by using our chat app. You can use the app to get answers to frequently asked questions, chat with a support agent, and search our knowledge base. For free, on-demand training, visit Starburst Academy. Docs: https://docs.starburst.io/starburst-galaxy/index.html Support Packages:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.


Standard contract
Customer reviews
Starburst’s Unified Catalog Makes Complex Cross-Source Queries Fast and Easy
Additionally, the dynamic schema discovery feature automatically detects table schemas for new data sources, which is incredibly helpful when working with constantly changing datasets in my organization. This saves hours each week that would otherwise be wasted setting up and maintaining connections to different databases manually.
Another pain point is the performance when handling extremely large datasets in real-time analytics scenarios. Although Starburst performs well under most conditions, there are occasional delays and scalability issues during peak usage times or when querying very large tables. This can impact productivity and user satisfaction, especially for time-sensitive projects.
Improving the onboarding experience through more interactive tutorials and guided setup wizards could alleviate some of these initial frustrations. Additionally, enhancing query optimization techniques and resource management features would help maintain performance even under heavy loads, ensuring a smoother user experience throughout all phases of data analysis.
Since implementing Starburst, we can seamlessly query across these diverse data sources through a single interface, which has dramatically improved our overall efficiency. Instead of spending days setting up connections and preparing datasets, I can now run complex analytics queries within minutes, supported by the unified catalog and dynamic schema discovery. As a result, we’ve achieved substantial time savings—about 30 hours per week across my team—and we’re able to spend more time on generating insights rather than managing data.
In terms of measurable impact, we’ve reduced project timelines by roughly 50%, and our ability to respond to business needs with real-time analytics has improved significantly. This strengthens decision-making and helps drive better ROI by enabling faster deployment of data-driven initiatives.
Fast, Practical Federated SQL with Great Integrations and Support
UI / UX
I liked the UI because it was clean and easy to use. It made common tasks feel straightforward, and I didn’t spend much time figuring out where things were.
Integrations
One of the biggest positives for me was the integrations. It connected well with multiple data sources, and that saved a lot of time in setup and day-to-day work.
Performance
Performance was a strong point. Queries felt fast, and that made analytics and reporting much smoother than I expected from a federated setup.
Pricing / ROI
From my perspective, the value came from reducing data movement and saving engineering time. Even if the pricing isn’t the cheapest, the ROI makes sense when you factor in faster access, simpler architecture, and less operational overhead.
Support / Onboarding
Onboarding was smooth, and the support was responsive when I needed help. The documentation and guidance made it easier to get up and running without a lot of friction.
AI / Intelligence
I also liked that Starburst is moving toward AI-ready use cases. The idea of combining federated data access with governance and AI-friendly features makes it feel more future-proof.
Overall, I’d describe Starburst as a strong platform for teams that want fast, federated SQL access across many systems, with good usability and solid support
UI / UX
The UI is usable, but it doesn’t completely hide the complexity underneath. When a platform has a lot of configuration and governance depth, the day-to-day experience can feel heavier than simpler tools.
Integrations
Even though the integrations are a strength, the breadth of connectors can also add operational complexity. In practice, that means there can be more moving parts to manage and more places where setup needs to be exact.
Performance
Performance is generally strong, but it isn’t always perfect for every workload. Some users mention slow queries or performance issues, especially when they’re asking very specific or demanding questions.
Pricing / ROI
Pricing is probably the part I’d question most. The value can be great at scale, but for smaller teams or tighter budgets, the cost can rise quickly as query volume grows.
Support / Onboarding
Onboarding is where I’d expect the most friction. It seems like you need enough technical depth to configure things correctly from the start, otherwise the learning curve can slow the team down.
AI / Intelligence
The AI direction is promising, but I’d still treat it as emerging rather than fully proven for every use case. For me, it wouldn’t be the main reason to choose Starburst today.
For me, the biggest benefit is that I can get a single SQL layer over distributed systems, which means less ETL, less duplication, and less time spent stitching data together manually. It also reduces operational overhead because I don’t need to build and maintain as much custom query infrastructure just to unify sources.
In practice, that means faster access to data, easier cross-system analysis, and quicker decision-making. It’s especially valuable when I need to join data from different platforms in real time or when I want to modernize an architecture without moving everything first.
The real value is not just convenience; it’s that Starburst helps keep data in place while still making it usable for analytics and AI workflows. That gives me more flexibility, better governance, and a cleaner path to scaling analytics without constantly rebuilding pipelines.