Starburst Galaxy

What do you like best about the product?

I love using Starburst because it makes it easy to run SQL queries, investigate issues, validate changes, and quickly gather information without moving data between systems. It's a useful tool for day-to-day development work and troubleshooting. The query editor is especially easy to use, making it simple to investigate issues and find data. Additionally, the initial setup was quite easy, and I had no issues setting it up. Starburst also provides a more unified way to query data across multiple sources.

What do you dislike about the product?

I find the user interface could be simpler, and better onboarding for new users would be beneficial. It would be helpful if the menus were simpler and user actions were easier to find. Interactive walkthroughs for new users would also be a great addition.

What problems is the product solving and how is that benefiting you?

I use Starburst to query and analyze data across different sources, making it easy to run SQL queries, investigate issues, and gather information without moving data. It offers a more unified way to query data across multiple sources.

What do you like best about the product?

I like that Starburst lets us query data across multiple sources without having to move everything into a central database. It’s fast and scalable, and it makes it much easier to access and analyze data from different systems in one place.

What do you dislike about the product?

Starburst is a powerful platform, but there are a few areas that could be improved. The initial setup and configuration can be complex, especially for teams that are new to distributed SQL or data lake architectures. Some parts of the UI could be more intuitive, as finding certain settings or monitoring query performance sometimes requires extra effort. While integrations with major data platforms are strong, configuring and maintaining them can take time. Performance is generally excellent, but complex queries may require careful tuning to achieve the best results. Pricing may also be a challenge for smaller organizations, as it can become expensive as usage grows. More guided onboarding, additional AI-powered query optimization suggestions, and clearer troubleshooting documentation would make the platform easier to adopt and manage.

What problems is the product solving and how is that benefiting you?

Starburst helps us analyze data across multiple data sources without having to move or duplicate it into a single warehouse. This reduces data silos, saves storage costs, and allows analysts to access up-to-date information more quickly. It has improved reporting efficiency, simplified data access for business users, and enabled faster decision-making by providing a unified view of data across different systems. The platform's scalability also makes it easier to handle growing data volumes while maintaining strong query performance.

What do you like best about the product?

What I like best about Starburst is how it simplifies querying and analyzing data across multiple sources without requiring data movement. The platform's data virtualization capabilities make it easy to access data from different cloud storage systems, databases, and data lakes through a single interface. The UI is intuitive and makes managing queries and datasets straightforward, even for users who are not data engineering experts.

Performance has been consistently strong, especially when working with large datasets, and the query optimization features help reduce processing times significantly. Starburst's integrations with major cloud providers and analytics tools make it easy to fit into existing data workflows. I also appreciate the governance and security features, which help maintain control over data access without adding unnecessary complexity.

Another major benefit is the cost efficiency gained from avoiding data duplication and reducing the need for complex ETL pipelines. The onboarding experience was smooth, and the documentation and support resources made it easy to get started. Overall, Starburst has improved our ability to access and analyze data quickly while maintaining performance, scalability, and strong governance.

What do you dislike about the product?

One downside of Starburst is that there can be a learning curve when setting up and optimizing more complex data architectures, especially for teams that are new to data virtualization concepts. While the platform offers extensive functionality, some advanced configuration and performance tuning options may require deeper technical knowledge to fully leverage.

The AI-powered features are promising, but they are still evolving. In some cases, AI-generated query suggestions or natural language capabilities may require refinement and validation before being used in production workflows. Users with highly specialized data environments may find that the AI assistance does not always fully understand their specific business context or data models.

In certain scenarios involving highly complex queries across multiple data sources, troubleshooting performance bottlenecks can take time. The user interface is generally well designed, but some administrative and governance features can feel overwhelming for new users during the initial onboarding phase. Pricing may also be a consideration for smaller organizations with limited data infrastructure needs, as the platform is primarily geared toward enterprise-scale workloads.

Although documentation and support resources are helpful, there are occasions where more detailed examples for advanced AI, governance, and optimization use cases would make implementation easier. Overall, these challenges are relatively minor compared to the benefits, but they are worth considering when evaluating the platform.

What problems is the product solving and how is that benefiting you?

Starburst helps solve the challenge of accessing and analyzing data that is spread across multiple systems, cloud platforms, and data lakes without requiring data to be copied or moved. Before using Starburst, working with data from different sources often required complex ETL processes and maintaining multiple data copies, which increased costs and introduced delays.

By providing a unified data access layer, Starburst allows us to query data from various sources through a single platform, making analytics faster and more efficient. This has improved productivity by reducing the time spent preparing data and enabling quicker access to insights. The platform's performance optimization capabilities also help accelerate large-scale queries, allowing teams to make data-driven decisions more quickly.

Additionally, Starburst improves data governance and security by providing centralized access controls across different data sources. Its integrations with existing analytics and cloud tools have made adoption straightforward, while the AI-assisted features help users discover data and generate queries more efficiently. Overall, Starburst has reduced operational complexity, lowered infrastructure costs, and enabled faster, more reliable analytics across the organization.

What do you like best about the product?

Connect everything—databases and cloud—faster. They eliminate the need to move data around. You can work with clean data and use natural language by asking simple questions, without any migration.

What do you dislike about the product?

Well, what I didn't like much is that the beginning is a bit difficult, you don't know how to unconnect and use it, not to mention that you need to know SQL-based systems. Since data streaming is very dependent on the network, if a database has a slow connection, the entire Stardust query will be slowed down.

What problems is the product solving and how is that benefiting you?

The main problem this helped me solve was the chaos of fragmented data—when information is spread across different servers and sources like AWS, Google Cloud, local databases, or even Excel. It supports real-time decision-making, boosts my productivity right away, and gives me strong control over my data.

What do you like best about the product?

I particularly appreciate the robust query capabilities and user-friendly interface of Starburst. The ability to execute complex queries against diverse data sources without writing extensive SQL code is a game-changer. For instance, I often need to analyze large datasets from multiple cloud storage systems simultaneously, and Starburst's unified catalog allows me to do this efficiently with just a few clicks. This significantly reduces the time spent on manual integration and data preparation tasks.

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.

What do you dislike about the product?

One area for improvement is the initial setup and onboarding process, which can be quite complex and time-consuming. Configuring the unified catalog requires meticulous attention to detail and an understanding of each data source's schema and metadata. While the documentation provides a good starting point, it lacks detailed examples for more advanced configurations involving third-party systems.

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.

What problems is the product solving and how is that benefiting you?

Before using Starburst, we struggled to integrate and analyze data from multiple, disparate sources, including cloud databases, SaaS applications, and on-premises systems. This fragmentation created inefficient workflows: team members spent a lot of time manually preparing, reconciling, and syncing datasets before any analysis could even start. Without a unified catalog, finding and accessing the most up-to-date data was cumbersome and frequently led to delays.

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.