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.
Starburst Galaxy
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Significantly improved our data architecture flexibility and performance management
What is our primary use case?
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.
Which deployment model are you using for this solution?
Public Cloud
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.
Unified Access to Data
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.
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.
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.
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.
Reliable way to retrieve and work with data efficiently
What do you like best about the product?
Probably the biggest standout from Starburst is that it decreases our dependency on operator data retrieval. We no longer have to extract and put things together from separate locations because it is accessible in a structured format. It has substantially reduced redundant work and made sure that our team dedicates its time to meaningful analysis rather than an excess on data preparation. This has helped us structure our decision making a lot more and has made it more efficient.
What do you dislike about the product?
Starburst is powerful, but some advanced query functions are as complex as you would expect for something that involves moving notoriously complex data. Not all team memebrs are comfortable with some features sometimes which sometimes causes to hindrance as we wait for someone who is more experienced. The documentation could also be clearer, most especially when trying to identify a particular scenario. Because of this lack of clarity, some processes have been slower than they should be.
What problems is the product solving and how is that benefiting you?
Starburst has saved a huge amount of time that we used to spend performing data preparation. This means we’re duplexing our limited time concentrating more on applying insights rather than piecing together information from a number of sources. It has proved a great advantage when working on cross functional projects as everyone has the same copy of the information to be able to work on. It has allowed us to work better with less of the usual bottlenecks.
Our team doesn't panic before data analysis anymore
What do you like best about the product?
One of the highlights of the Starburst architecture is its data lakehouse architecture. This enables us to query data between different formats and locations without any issues. This has been a lifesaver for our data analysts so that they can have faster access to and analysis of data. The cost based optimization feature is also good since it helps us manage and optimize query costs. Moreover, it is easy to integrate with different data sources for the purpose of data consolidation from different platforms, offering a single view.
What do you dislike about the product?
Setting up Starburst is a bit challenging, especially for those who are new to data lakehouse concepts. Although the documentation is comprehensive, the huge beginner friendliness. In addition, it may take a while to learn the curve of optimizing queries, and you can certainly need some trial and error to achieve the best performance.
What problems is the product solving and how is that benefiting you?
Starburst provides the solution to siloed data and slow query. Our data had been scattered across different platforms beforehand, and was not easy to get a holistic view of before using it. With Starburst, we can now query many formats and locations, offering a single, full view. This has greatly contributed to having better data analysis and making decisions. To give an example, previously, with hours, we used to run complex queries across different data sources. But now in minutes, we can run such queries.
Our team's stress levels have dropped since we started using Starburst
What do you like best about the product?
Starburst’s query engine is very powerful. This allows me to do such complex queries on mutiply data sources without lifting a finger. This is a game changer for my work with my data analysis tasks. It is impressive that the software handles large datasets efficiently. For my work, I can process and analyze lots of data but will never get a lag. Furthermore, the user friendly interface of the website makes it easy for the user to navigate and use even if one is not so tech savvy. Top notch with regards to integration capabilities as well.
What do you dislike about the product?
Starburst can handle robustly, but for beginners, it is steep learning curve. On this matter, the documentation is very complete, but for new users it can be quite overwhelming. It is also a bit confusing in pricing structure as different tiers come with different access to the features. A standard price model would be really good. Finally, the customer service is too slow to respond. Sometimes I had to wait for a longer time than I was expecting to receive help.
What problems is the product solving and how is that benefiting you?
It was a difficult, time consuming and frustrating process to analyze large datasets before the advent of Starburst. To achieve this, I had to use a lot of tools and manual processes, that were slow and error prone. However, with Starburst, I can run complex queries and analyze data from other sources in the same place. It has made a huge difference to process my work. To give an example, before I used to export data from various sources and later, for analysis, I had to manually combine them.
Great for complex queries
What do you like best about the product?
I like to see how Starburst makes it easier to link disparate data sets. Despite this, it keeps working, running smoothly, and maintaining standard and precision in every organization even when dealing with different environments. The use of research has helped in minimizing the risks posed by incomplete and other mismatched data results. The flexibility it has shown in dealing with the different types of queries it receives has helped minimize on overly complicated intra-functional work flows while directing more effort to the production of relevant results.
What do you dislike about the product?
One major inconvenience of Starburst is that during heavily utilized periods, when executing complicated queries, it draws a rather large number of resources, and hence can be slow. Because fixing these queries involves some high level of technical experience, the problem solving would take a lot of time consuming time than required hence the disruption of the normal working progress hence affecting time bound projects that would be very crucial in the achievement of business objectives.
What problems is the product solving and how is that benefiting you?
Starburst has addressed the problem of having different sources of data where the user has to develop different pipelines to handle them and this has reduce a lot of time and energy. It relieves our team of managing infrastructural issues that would otherwise distract from creating the desired impact. The search and handling of data from different sources have become easier and efficient due to Starburst making our outcomes more accurate and our programs more on schedule.
Our team’s data access has never been easier
What do you like best about the product?
The thing that I like most about Starburst is that it gives you instant access to all kinds of data across all sorts of places without ever needing to move the data physically. The ability for us to pull insights directly from where the data lives greatly benefits our workflow. By not having to manually collect, transfer, or aggregate data before using it, we’ve been able to streamline our processes and save us a lot of very valuable time.
What do you dislike about the product?
In sum there are many good things with Starburst search engine but it is not always suitable for task of executing very specific, detailed questions. In some cases when a certain level of depth or specificity is required for business operations we have had to turn to other tools. Sometimes this longer process can be quite bothersome, especially when one has to shift between the tools for the purpose of the different interfaces.
What problems is the product solving and how is that benefiting you?
Starburst helps us solve one of its main challenges – use of dataset across multiple platforms for analysis becomes challenging. We found ourselves daily shuffling and merging data without the use of It which was greatly involving and full of errors. Now with Starburst we can go direct to where the data sits and it has helped to do a lot of these things for us, so we are more efficient and pretty much get accuracy in our working which has led to making quicker decisions.
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