Starburst Galaxy

What do you like best about the product?

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.

What do you dislike about the product?

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.

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

By using Starburst we have removed the problems or fragmented data across many platforms as we can query everything through a single interface. By cutting down on the manual release of data from separate systems, we’ve saved ourselves a lot of time, and workflow has been streamlined with the data made easier to access. It’s also allowed us to work better with data, through more efficient use of data to work faster on the whole company and faster to make decisions on data.

What do you like best about the product?

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.

What do you dislike about the product?

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.

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

Starburst has changed the game for us, out of data relevant to us now comes automatically and effortlessly. Instead of dunns between platforms to get to all the information we need we no longer waste time switching between platforms. All that is now accessible in the area of one place, and makes data analysis faster and more efficient. Moreover it’s helped with teamwork and fasten made making decisions suitably.

What do you like best about the product?

Really appreciate the quick turnaround time for resolving any issues.

What do you dislike about the product?

The pricing is much on the higher side. Plus when it comes to renewal the annual escalation is high.

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

We have huge data of user activities playing different types of games on our various versions of app on android, iOs etc. Through distributed processing, Starburst has been able to provide optimal performance even with immensely high data volumes.

What do you like best about the product?

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.

What do you dislike about the product?

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.

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

Enabling Data Scientists and DataEngineers to run Large analytical workloads and ad-hoc querying.

What do you like best about the product?

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.

What do you dislike about the product?

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.

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

Data Silos
Starburst allows querying data in place, eliminating the need for traditional ETL pipelines that move data into a centralized repository. This reduces latency and complexity.

Performance Bottlenecks in Distributed Queries
Leveraging Trino’s high-performance distributed SQL engine, Starburst enables interactive and scalable analytics over large and diverse datasets.

Lack of Unified Access Across Cloud and On-Prem
With Starburst, we can connect to multiple data sources (across AWS, Azure, GCP, on-prem DBs) through a single SQL interface, simplifying analytics and reducing tool sprawl.

Data Governance and Security Challenges
Starburst supports fine-grained access control, auditability, and role-based permissions—essential for compliance and enterprise data governance.

Tool Fragmentation and Analyst Productivity
Analysts and data scientists can use standard BI tools like Tableau, Power BI, or even Jupyter with Starburst to access all relevant data without switching contexts or learning new interfaces.