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.

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.

- True data federation that enables querying and joining data across diverse sources without data movement or duplication.

- High-performance distributed SQL engine that delivers fast, secure, and governed access to enterprise data at scale.

- Open lakehouse architecture with strong support for Iceberg and cloud-native ecosystems, reducing vendor lock-in and improving flexibility.

- Continuous innovation through capabilities like AIDA, Starburst Agents, MCP, and Data Products, positioning the platform well for AI-powered data engineering and analytics. Review collected by and hosted on G2.com.

- Connector capabilities can be inconsistent, with some source-specific database features and metadata not fully supported across all connectors.

- Troubleshooting complex queries can be challenging, as error messages and diagnostics could provide more actionable insights for faster root cause analysis. Review collected by and hosted on G2.com.

The federated query layer — one SQL surface over S3/Iceberg and Redshift — combined with a solid public API that lets us automate the whole cluster/catalog lifecycle for self-service onboarding, on managed elastic compute we don't have to operate. The AI capabilities are an add-on advantage Review collected by and hosted on G2.com.

Currently rough edge is managing catalogs at scale (per-cluster limits and some API latency), but it's easy enough to work around.The pricing runs a little on the higher side, so cost is something we keep an eye on as usage grows Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Starburst solves the challenge of ingesting streaming security data via schema discovery while allowing querying and enrichment from federated catalogs at near real time and allows the collapsing of multiple applications into one product thereby reducing cost. Review collected by and hosted on G2.com.

Starbursts own application logging could be more detailed and easier to export for historically lookup and review. When a query fails and you use the advance tab to look into specific Trino errors at run time, there is additional steps to troubleshoot/discover what they suggest as the issue. Review collected by and hosted on G2.com.

What I like most is that Starburst makes it easier to work with data located in different places. Instead of constantly transferring and copying data, it is possible to simply access different sources through a single interface. This way, teams can quickly get the information they need and work more efficiently with large amounts of data. Review collected by and hosted on G2.com.

What I like less about Starburst is that it takes a little time for users to get accustomed to all the features of the platform. For new users, it can seem complex at first, especially when dealing with larger amounts of data. I wish there were more simple guides and examples for everyday user scenarios. Review collected by and hosted on G2.com.

The support team is very good. They are very helpful, quick and professional. Their prompt assistance and dedication make a real difference, and their efforts are truly appreciated. Review collected by and hosted on G2.com.

. The only area I feel could be improved is the documentation, which could be more detailed and easier to follow. Other than that, I have no further concerns. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.