I have different data sources, including Oracle, DB2, and a MongoDB cluster, so I join all of these data sources using Starburst Galaxy with the federated querying feature. I transform that into Iceberg using Starburst Galaxy, land it in S3 storage, convert it into Iceberg tables, and then use them for dashboarding in Power BI or Tableau.
Starburst Galaxy
StarburstExternal reviews
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Unified data querying has accelerated petabyte-scale analytics and simplified dashboard delivery
What is our primary use case?
My main use case for Starburst Galaxy is querying petabytes of data across vast data sources, and I use a federated query engine to join data sources from different databases and then join them using Starburst Galaxy.
What is most valuable?
Starburst Galaxy offers me several best features, which include very fast querying results, automatic indexing of data for long tables, a cost-based optimizer which reduces the time to query large tables, and an agentic feature that lets me talk to my data.
I find myself relying most on querying from different databases as well as automatic indexing in my day-to-day work, as I am a data science architect who needs to get the queries in a very short period of time. Starburst Galaxy serves the best purpose for me because if my SLAs are not met with my customers, they will raise a case, and I have tried many other tools, but Starburst Galaxy fits the best.
Starburst Galaxy has positively impacted my organization since we were struggling with Denodo and Dremio, which had their own features but were not helpful in querying large amounts of data, especially semi-structured or unstructured data. Starburst Galaxy addresses this with many YAML files and manifest files for automated maintenance, and it helps reduce the small file problem in different HDFS systems. Additionally, Starburst Galaxy has an MCP server that connects to various agentic pipelines, reducing the time to market for data consumption.
What needs improvement?
Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose. We rely on third-party tools for ingesting the streaming files, and I see they are integrating with MCP and agentic pipelines. Including these features would make Starburst Galaxy a much better tool.
For how long have I used the solution?
I have been using Starburst Galaxy for the last four years.
What do I think about the stability of the solution?
Starburst Galaxy is stable.
What do I think about the scalability of the solution?
Starburst Galaxy's scalability is excellent as it can easily scale up to large clusters, with many nodes configured for the amount of data ingested daily, allowing it to handle petabytes of data efficiently.
How are customer service and support?
Customer support is quite good; we faced issues with complex queries and reached out to Starburst, and they were very helpful in troubleshooting those issues.
Which solution did I use previously and why did I switch?
We previously used Dremio, but it was not fast and had a lot of limitations in processing large unstructured and structured data, leading us to switch to Starburst Galaxy.
How was the initial setup?
The experience with pricing, setup cost, and licensing was straightforward, as I could easily purchase the licensing in the AWS Marketplace. For on-premises, we contacted Starburst for licensing costs, and the setup was quite easy—a one-click setup that allows us to discover the catalogs for querying. The licensing cost was reasonable compared to the value we receive from Starburst Galaxy, making it a good product overall.
What was our ROI?
I have seen a return on investment; the quick dashboarding allows me to publish dashboards and data products, saving significant time for publishing to different endpoints and translating into a cost savings of around $500,000, which we previously spent on getting reports published due to the reduced turnaround time and our capability to serve many more customers than before.
Which other solutions did I evaluate?
We did not evaluate other options before choosing Starburst Galaxy; we directly went to it.
What other advice do I have?
I would advise others looking into using Starburst Galaxy to consider it one of the best tools in the market, especially for ingesting large datasets and federating queries across different data sources. Starburst Galaxy is the best engine currently available, and they should try it for themselves to see the difference in query times. My overall review rating is 8 out of 10.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Unified data from diverse sources has created consistent client views and reshaped data strategy
What is our primary use case?
My main use case for Starburst Galaxy is to use it as a data federation tool, collect data from various data sources, and have a unified view of the data.
A quick specific example of how I use Starburst Galaxy for data federation in my daily work is that I assume I need data from five different data sources, and each data source is on a different database platform, and I have information that I need for my client profile. I can pull data from all those five different data sources and have a consolidated view of the client.
Those are the main use cases for Starburst Galaxy; basically, we are trying to build data products.
What is most valuable?
Starburst Galaxy is very SQL friendly, which stands out for me because I have used SQL in other platforms such as SQL Server, Teradata, and Oracle, so it is very portable with minor changes.
Another feature I appreciate in Starburst Galaxy is that it has object storage with Iceberg storage, which helps optimize data storage and also enables columnar search, which speeds up queries.
Starburst Galaxy has positively impacted my organization by allowing us to rethink the strategy for data and architect data differently; instead of having multiple data marts and siloed data marts, we have a unified vision, and that is how it is changing.
What needs improvement?
One way Starburst Galaxy can be improved is through AI enablement. I have not seen how the user interface is going to function or how users can interact with the data products on Starburst Galaxy using AI, so I am curious to know about that.
I chose a rating of eight because it has many good features, including data federation and the ability to write queries easily. I think there are areas of improvement with respect to AI adaptability, and also in general, the amount of connectors working with other tools are areas where it can be expanded.
For how long have I used the solution?
I have been using Starburst Galaxy for 18 months.
What do I think about the stability of the solution?
Starburst Galaxy is stable in my experience so far.
What do I think about the scalability of the solution?
I do not have enough visibility into the scalability of Starburst Galaxy, but I think we are adding more and more data sources into it, so I believe it is going to be scalable, though results are still pending.
How are customer service and support?
Starburst Galaxy customer support is good.
Which solution did I use previously and why did I switch?
Earlier, we were using traditional databases.
What was our ROI?
I am yet to see the hard numbers regarding return on investment, but I believe it will probably result in money saved and time saved.
What other advice do I have?
My advice to others looking into using Starburst Galaxy would be to first understand your current data environment and make sure that you have the right connectors that Starburst Galaxy can connect to those environments. Have a dedicated team from Starburst who can help you through all the installation and onboarding, and ensure all your personnel who are going to be working on that environment receive good training with proper use cases. I would also recommend using a sandbox in your environment and putting Starburst Galaxy in it so that you can get a taste of how it works with your data. I gave this product a rating of eight.
Outstanding Performance and Savings with Robust Governance
What do you like best about the product?
The query performance, governance features, and cost savings stand out when compared to other solutions such as AWS Athena.
What do you dislike about the product?
The user interface could use a more modern design and enhancements to make it easier to use. Additionally, the existing documentation is too basic for enterprise needs; users would benefit from more advanced, production-level examples instead of just beginner tutorials. Finally, the consumption-based pricing model makes it difficult to predict monthly expenses with accuracy.
What problems is the product solving and how is that benefiting you?
This product offers capabilities in data analytics, cataloguing, and data governance. It provides tools that help manage and organize data efficiently, supporting both analysis and oversight. Overall, it addresses key needs in handling and governing data within an organization.
Streamlined Data Analytics with Excellent Support
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
Starburst solves data duplication issues, allowing access to any source without copying data for analytics. It's user-friendly for managing data security with RBAC and facilitates complex queries across multiple data sources, supporting multilingual analytics.
Effortless Data Federation and Granular Governance Made Easy
What do you like best about the product?
Effortlessly federating data is a standout feature, and the ability to apply data governance with a high level of granularity is impressive. I also appreciate how easy it is to use overall.
What do you dislike about the product?
One drawback is the absence of a built-in data processing and orchestration mechanism.
What problems is the product solving and how is that benefiting you?
The platform enables rapid data integration from a variety of sources, spanning different regions and cloud environments. It simplifies feature engineering for AI datasets, making the process more efficient. With a single platform, it delivers governed data to multiple stakeholders, ensuring consistency and control. Automated user access is streamlined through OKTA SAML setup, enhancing security and ease of use. Additionally, curated data can be provided directly to client storage, eliminating the need to store or transfer data locally.
Effortless AI Agent Creation with Robust Features
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
Creating data pipelines and ease of connecting AI agents with organisational data.
Significantly improved our data architecture flexibility and performance management
What is our primary use case?
My team uses Starburst Galaxy for cross-database querying, iceberg table management, and workload separation across multiple data sources. We implemented Starburst Galaxy to replace our self-hosted Trino setup, bridging gaps in our data warehousing situation where we need flexibility to read from various warehouses and write to different formats while maintaining clean compute separation.
How has it helped my organization?
Starburst Galaxy has significantly improved our data architecture flexibility and performance management. We have successfully solved cross-database query challenges by utilizing Starburst Galaxy's ability to read and write in iceberg format on Trino, making our iceberg tables usable externally across our entire data ecosystem.
The compute separation capabilities have been transformative. We can easily split workloads and prevent sporadic usage spikes from slowing down critical processes. This has resulted in much more predictable performance and better resource utilization across our data operations.
The clean entry point provided by the built-in query engine has streamlined our SQL development workflow, while the data products functionality gives us an excellent way to present our end-state warehouse-level tables to stakeholders.
What is most valuable?
The flexibility to connect to numerous different warehouses and write to various formats is Starburst Galaxy's standout feature. This adaptability allows it to mold itself perfectly to our specific needs rather than forcing us to conform to rigid constraints.
The compute-focused architecture makes workload management incredibly straightforward. Since Trino focuses primarily on compute, it is really easy to work with and optimize. The user interface for navigating, managing permissions, and viewing queries and clusters is excellent and makes administration tasks much more manageable.
Cross-database functionality combined with iceberg format support has been game-changing for our data integration workflows.
What needs improvement?
For teams heavily invested in cutting-edge dbt features, it is worth noting that Starburst Galaxy is not a tier 1 dbt partner, so it is typically slower to adopt the newest dbt capabilities such as the Fusion Engine and Semantic Layer. While these features would be nice to have, it was not significant enough to deter us from choosing Starburst Galaxy. The core functionality works well and the benefits far outweigh this limitation.
Cluster startup time is another pain point, typically 3 to 5 minutes, which is not the worst with proper planning but can be annoying for ad-hoc work. The lack of a Terraform provider is also a notable gap for infrastructure-as-code workflows. Additionally, integration between data products and the dbt Semantic Layer would significantly enhance the platform's value proposition.
For how long have I used the solution?
We have used Starburst Galaxy for a few months.
Which solution did I use previously and why did I switch?
We migrated our self-hosted Trino instance to Starburst Galaxy.
What's my experience with pricing, setup cost, and licensing?
Pricing is competitive and the value proposition depends on your specific use case and requirements. When evaluating against alternatives such as Snowflake, it is worth considering the unique flexibility and cross-database capabilities that Starburst Galaxy provides rather than focusing solely on compute costs.
Which other solutions did I evaluate?
We briefly explored other options, but given the one-to-one nature of Trino and Starburst Galaxy, it made for a more seamless transition.
What other advice do I have?
Starburst Galaxy excels as a flexible, adaptable solution for teams dealing with complex, multi-source data architectures. It may not be the absolute best at any single function, but its strength lies in being very good at many things while remaining highly malleable.
I would particularly recommend it for teams that need cross-database functionality and iceberg format support, though dbt-focused teams should be prepared to work around the slower adoption of cutting-edge dbt features. It is important to plan for cluster startup times in your workflows, and if infrastructure-as-code is important, factor in the current lack of Terraform support.
Overall, if you are looking for a solution that can bridge gaps in your data architecture rather than replace everything, Starburst Galaxy is an excellent choice that provides the flexibility to adapt to your specific needs.
Now we can work much more efficiently with data
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.
More than meets the requirement
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.
Fantastic product from the creators of Trino!
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.
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.
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