Starburst Reviews & Product Details

useless never used it I only did the review because g2.com asked and they was paying 20 or 25 euros through an ad they sent through linked in I never did any querying on its interface or ui Review collected by and hosted on G2.com.

all good I guessdddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddd Review collected by and hosted on G2.com.

Starburst isn’t a tool I’ve had a lot of exposure with since deployment, but I’ve always been impressed with how the tool works and integrates with so much. Review collected by and hosted on G2.com.

Documentation is improving but always wise to keep it moving Review collected by and hosted on G2.com.

I like how Starburst gives us more insight about the query execution plan. It's really helpful in understanding how a query is going to execute. It also has lineage and audit features. I find it user-friendly for managing data security with RBAC and based security controls, which are necessary for data governance. Starburst is especially useful for running complex queries, especially when dealing with multiple joins and data source systems. It supports multiple languages, which I find very valuable. Setting up Starburst wasn't complex, and I had good support from the solution architect, which made the process successful. Review collected by and hosted on G2.com.

Sometimes I face challenges with the query editor. Whenever we have a big source code, it's not big enough to troubleshoot and run the query faster compared to a traditional SQL editor. I would definitely give feedback to the internal team to make it more user-friendly. Review collected by and hosted on G2.com.

Cleaner sharding and better autoscaling Review collected by and hosted on G2.com.

Nothing at the moment that would be needed Review collected by and hosted on G2.com.

While creating AI agents its very easy to use starburst it already has most support and nice number of features. If you want to let AI agents use your data there are built in plugins controlling governance and ease. Review collected by and hosted on G2.com.

Lack of community support and reliability. Most of the companies prefer to go to Azure and have their own AI solution because main thing is about reliabilty. Review collected by and hosted on G2.com.

Starburst improve the response time and incorporated AI/ML capability. Review collected by and hosted on G2.com.

My previous comment still stands. In addition, while Starburst has improved response time, certain connectors, like SQL Server, is single-threaded so the performance is lacking in comparison to other connectors that can do parallelization. Review collected by and hosted on G2.com.

Starburst has the speed of serving complex queries. I’ve found that it optimizes query execution whether the data is in multiple systems. And it helped reduce the hassle of asking people for data and querying it manually, which had skewed to a massive part of our time. Having the ability to take complex join queries, without worrying about movement of data has really elevated our workflow to be able to see more analysis instead of thinking about logistics. Review collected by and hosted on G2.com.

To be fair, Starburst is a very full featured product and pretty intuitive but some things are not so intuitive as I’d like them to be so troubleshooting can be more difficult. Moreover, when it comes to distributed query engines, those teams that are new to them will need some time to really get a whole picture of all the choices here offered. Occasionally, on a problem, I get so lost in the docs and have to dig it that we can’t work. This could certainly be made clearer and more accessible. Review collected by and hosted on G2.com.

The best thing about Starburst is how it manages such complicated data environments. It lets us query data in multiple system data, AWS and google cloud without having to worry about integration issues. It’s also fast, and that worked even with large amounts of data, which is the performance, and the results. Making our data access just much easier and indispensable for our analytics team. Review collected by and hosted on G2.com.

For some reason, the initial setup for Starburst is the main downside I’ve found to it. It’s strong, but a lot of configuration is needed when combining with other data sources. Assuring that everything is attached and working in concert can take some time. Furthermore, there are some issues with dealing with bring complex queries, which can cause a performance hiccup when trying to pull all the data from multiple sources at once. Review collected by and hosted on G2.com.

Starburst provides a single point of access to multiple disparate data sources including data lakes, cloud storage, relational databases, and NoSQL systems. This significantly simplifies the analytics process by allowing users to query data in-place, eliminating the need for complex ETL pipelines.

The Security and Governance features are great which helps in auditing etc.

1. Unified Access to Distributed Data

Starburst provides seamless connectivity to a wide range of data sources—relational databases, data lakes, cloud storage, and more—allowing analysts and data scientists to query across silos using standard SQL.

2. High Performance & Scalability

Built on Trino’s distributed SQL engine, Starburst offers fast query performance at scale. Features like cost-based optimization, dynamic filtering, and query caching significantly enhance performance for large datasets.

3. Enterprise-Ready Security & Governance

Starburst integrates with authentication systems and supports fine-grained access control via tools like Apache Ranger, making it a secure option for highly regulated environments.

4. Flexible Deployment

Supports hybrid, multi-cloud, and on-prem deployments. With Starburst Galaxy, the fully managed SaaS offering, users can get started quickly without infrastructure overhead.

5. Broad Ecosystem Support

It integrates with major cloud platforms (AWS, Azure, GCP) and connects with popular BI and data tools such as Tableau, Power BI, and dbt. Review collected by and hosted on G2.com.

While Starburst is a strong platform, there are a few areas that could be enhanced:

1. Catalog Creation and Management

Setting up catalogs, especially at scale, can be complex and sometimes unintuitive. Improvements to the user experience, automation, and management of catalogs—particularly in large, dynamic environments—would greatly benefit data engineering teams.

2. Learning Curve for New Users

Though SQL-based, the platform requires understanding of distributed query execution, connector configurations, and performance tuning. Organizations may need to invest in training or initial consulting support.

3. Monitoring and Troubleshooting

While Starburst provides basic query monitoring tools, more advanced observability features (e.g., deeper lineage tracking, proactive performance insights) could further simplify troubleshooting and operational efficiency.

4. Cost Management in Cloud Environments

Given the platform’s power, it’s easy to incur high compute costs when querying large datasets across multiple cloud sources. Resource management policies need to be carefully implemented. Review collected by and hosted on G2.com.

Starburst was one of the first to offer Fault Tolerant Mode(FTE) with Trino and it enabled us to execute larger queries without any time limit as imposed by other similar query engines like Athena. Starburst SSO integration and SCIM capabilities along with the IAM integration with AWS was very well implemented. RBAC and ABAC capabilities are also robust. Starburst support team has also been very responsive so far. Review collected by and hosted on G2.com.

Telemetry capabilities are still far from robust. CPU based autoscaling had a lot of issues, but has greatly improved recently. Lack of integration with alerting tools like slack or pagerduty is very limiting and making users to spend time integrating custom plugins. There is also no current feature to warn users if certain queries are blocking the cluster resources or some automatic termination of such queries. Review collected by and hosted on G2.com.