What I like most is being able to query disparate data silos with standard SQL without having to move the data. This has significantly reduced data duplication and improved time-to-insight for our analytics team. The managed setup in Starburst Galaxy has been reliable for us, is straightforward to monitor, and scales smoothly with our query workloads. Review collected by and hosted on G2.com.

Cost tracking and resource consumption can ramp up quickly if cluster sizing and auto-suspension policies aren’t carefully managed, especially during peak query periods. Dynamic scaling is definitely convenient, but I’d like to see more granular, out-of-the-box cost-allocation and budgeting dashboards at the workspace level to make administrative oversight simpler. Review collected by and hosted on G2.com.

We originally had our data lake built on a Hive engine. One of the tings we like about Starburst Enterprise is the improvement it provided to our consumers by providing a powerful distributed SQL query engine built on a Trino engine, allowing fast querying across diverse data sources.

We also shifted away from an Enterprise Data Lake secured Privacera Ranger to a Data Mesh architecture governed by AWS Lake Formation. From a business and customer support perspective, Starburst were attentive to our technical/business requirements, feature requests, etc. Review collected by and hosted on G2.com.

One area of improvement for Starburst is to consider the rollout of GA features after undergoing somewhat further rigorous/comprehensive use case testing. Or alternatively, perhaps be more mindful with the language of GA vs Public Preview vs Private Preview. Review collected by and hosted on G2.com.

I really appreciate how Starburst makes it easy to query data from different sources in one place, which speeds up data analysis and saves time by reducing the need to move data around. It fits well into our existing data workflow and integrates smoothly with our other tools and cloud storage platforms, offering more flexibility than our previous setup. The initial setup was fairly straightforward, allowing the team to start using it without too much trouble. Review collected by and hosted on G2.com.

The learning curve can be a little steep at first, especially for new users. Some setup and configuration could also be more straightforward. I think the onboarding could be simpler, with clearer documentation and more step-by-step setup guides. Better examples for common use cases would also help new users get started faster. Review collected by and hosted on G2.com.

I like Starburst’s ability to query data across multiple sources without having to move everything into one place. The SQL experience is familiar, the platform works well with different data sources, and it makes accessing and analyzing distributed data much simpler. It also helps reduce the time spent preparing data before analysis. Review collected by and hosted on G2.com.

The initial setup and configuration can feel a little complex, especially when connecting multiple data sources. Some settings and troubleshooting steps could also be explained more clearly in the documentation. A simpler onboarding experience and more guided configuration would make the platform easier to adopt. Review collected by and hosted on G2.com.

I like that Starburst makes it easy to access and analyze data across different sources as it's fast and reliable, simplifying our data workflows without adding complexity. The federated query capabilities, data source connectors, and fast SQL-based analytics are features I especially value, as they make it easy to work with data across multiple systems without having to move everything into one place. I also appreciate how Starburst connects data from different sources, allowing us to query it in one location, making data access simple, fast, and efficient. Plus, its ability to integrate seamlessly with our existing databases and BI/analytics tools using SQL has made it a perfect fit in our data stack. Review collected by and hosted on G2.com.

The overall experience is good, but the initial setup and configuration can take some time. More beginner-friendly documentation and simpler configuration options would make it easier for new users to get started. Review collected by and hosted on G2.com.

I like that Starburst lets us easily access and query data from multiple sources, simplifying data analysis and improving accessibility. It speeds up insights without having to move data around. I also appreciate its flexibility and performance, as it integrates well with different data sources, which makes it easier for our team to work with data efficiently. The initial setup was fairly straightforward, and I find the documentation and configuration options helpful for getting started. Overall, it’s a reliable and flexible solution that makes working with data from multiple sources much easier. Review collected by and hosted on G2.com.

The initial learning curve can be a bit steep for new users. The interface and some advanced configurations could be more intuitive. The interface could be more intuitive by simplifying navigation and making common tasks easier to find. Clearer setup guidance, better documentation, and more straightforward configuration options would also help new users get started faster. Review collected by and hosted on G2.com.

Service task overview not provided in the input. Review collected by and hosted on G2.com.

What I like most about Starburst is the freedom to query from anywhere. There’s no vendor lock-in, no need to duplicate data, and no waiting around for pipelines to finish. I can just query what I need, right where it already lives. Overall, it feels fast, clean, and simple. Review collected by and hosted on G2.com.

The web console and management interface provide clean visibility into cluster health, query execution paths, and resource usage. Navigating the UI makes it straightforward to monitor active workloads, administer fine-grained access control, and manage data products from a single pane of glass without jumping across disparate cloud environments. Good project. Review collected by and hosted on G2.com.

Cost and Resource Tuning While the performance is exceptional, optimizing resource consumption and memory limits for massive, highly concurrent federated queries requires deep tuning expertise. Misconfigured queries or missing pushdowns can inadvertently trigger high cloud infrastructure costs. Additionally, enterprise licensing and managed service pricing can scale steeply for smaller teams, making it a platform best suited for large-scale data environments. Review collected by and hosted on G2.com.

The best features of Starburst that I like most are its multi-source data access and the performance of the Trino engine. Without needing data migration, queries run super fast and workflows get streamlined. The AI-driven query optimization is an unexpected bonus that helps reduce my workload. Review collected by and hosted on G2.com.

Starburst’s pricing feels steep for smaller teams, the advanced setup can be complicated for non-technical users, and the dashboard customization options are fairly limited, which reduces overall flexibility. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.