I use Starburst Galaxy to support interactive queries and dashboards.
When comparing it to Databricks, which is also deployed to serve ETL pipelines, Starburst is much faster and much more friendly to non-technical employees.
Starburst Galaxy
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Supports interactive queries efficiently with fast query completion for better access to data
What is our primary use case?
How has it helped my organization?
Starburst is the most important portal for both technical and non-technical employees to access the data lake.
Starburst also provides a user-permission system which protects sensitive data.
What is most valuable?
The most fundamental feature is the query engine, which is much faster than any of the competitors.
Starburst is able to finish most queries within 10 seconds, which is especially important for many non-technical employees.
What needs improvement?
I would like Starburst to leverage AI to improve usability.
Data lakes are complicated and difficult for users to explore. AI would help a lot in this respect.
For how long have I used the solution?
I have used the solution for over three years.
Which solution did I use previously and why did I switch?
I used other tools before, and I switched because Starburst is faster.
What's my experience with pricing, setup cost, and licensing?
The price is reasonable and controllable.
For example, you have a fixed-size cluster and the cost is predictable. Queries may slow during rush hours, but there is no spike in billing.
Which other solutions did I evaluate?
I evaluated other solutions such as Databricks.
What other advice do I have?
I wish there were more products available in the ecosystem.
Federated querying delivers integrated data at record speed and reduces processing time
What is our primary use case?
We use Starburst Galaxy to query data across our diverse data ecosystem. Our data has evolved over many years and is spread across many data sources. Starburst enables us to query across this ecosystem without having to move everything into a single location.
Our teams require a method for integrating data from various systems for reporting and ad-hoc analysis, and Starburst Galaxy fundamentally meets this need.
How has it helped my organization?
The biggest win has been the ability to combine data from multiple sources and deliver it to the business at record speed.
This capability has allowed us to query directly through Starburst Galaxy, enabling teams to access integrated data that would otherwise be hard to pull together.
This has reduced both our ETL processing time and storage costs. We are answering questions that would have been hard, if not impossible, to answer previously because the data came from disparate, disconnected sources.
What is most valuable?
Federated querying through Starburst Galaxy has unlocked our ability to move data using SQL, keeping data in the data layer. The ability to use SQL to query multiple data sources and then write to a single destination has been essential.
Additionally, setting up new data connections is straightforward.
What needs improvement?
I would like to see per-model cluster routing selection when using dbt. Cluster startup time can be slow, sometimes taking over a minute.
For how long have I used the solution?
We have been using the solution for 6 months.
Which solution did I use previously and why did I switch?
We started using Trino, which worked, but we wanted a reliable managed solution to help us scale.
What's my experience with pricing, setup cost, and licensing?
The pricing is transparent and reasonable.
Which other solutions did I evaluate?
We considered using open source Trino.
What other advice do I have?
Starburst Galaxy addresses our primary problem of managing and working with data spread across multiple systems. Our teams can access and combine data from any source, enabling faster insights and reducing the time spent on manual data wrangling.
Starburst Galaxy is becoming a cornerstone of our data platform, empowering us to make smarter and faster decisions across the organization.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Has a cost-effective transformation for data management as efficient querying enhances productivity
What is our primary use case?
Our primary use case is to manage hundreds of terabytes of data efficiently across a wide range of internal use cases, including ingestion/ETL, machine learning pipelining, and customer-facing product workflows.
It is a top priority to enable all engineers to have access to this volume of data without the concern of overspending on expensive cloud warehouse providers.
How has it helped my organization?
We have experienced several improvements across our organization.
Our data ingestion processes previously involved copying data from S3 to Snowflake, which was fairly costly and required constant vigilance to purge old data so that our source tables would not bloat.
Now we are able to move ingestion staging data to Iceberg tables, resulting in a much better experience in terms of both compute and storage costs as well as maintenance.
Data transformation has also become more efficient.
Starburst on Trino, combined with our SQL-native data transformation tool SQLMesh, has delivered anywhere from a two to five times improvement in compute performance across our transformation DAG.
This improvement is largely due to how efficiently Trino scans relevant data without requiring any additional setup, such as defining partitions in Snowflake.
In terms of cost effectiveness, we are already forecasting a 25% reduction in cloud data provider spending, even while continuing to use both Snowflake and Starburst.
This is because we are able to shift a significant amount of compute to Galaxy, and the cost difference compared to our previous approach of running jobs exclusively on Snowflake is substantial.
What is most valuable?
Cross-catalog querying and compatibility with AWS Glue have both significantly enhanced the user experience.
We operate several accounts within our AWS organization, each containing substantial volumes of data, and the onboarding process with Starburst has been fairly quick, even in the face of AWS IAM complexities.
What needs improvement?
The most persistent issue is the cluster spin-up time.
Coming from Snowflake, where warehouse spin-ups are nearly instantaneous, it has been a challenge to adapt.
However, I believe the Starburst team is working on solutions for this.
Additionally, the cluster and query monitoring UI lacks an optimal user experience.
I would recommend that the Starburst team invest in forking the Trino console and enhancing that tool, as observability is very important to us.
More Starburst-specific documentation would also be helpful.
I understand that some Trino functionality, such as certain parameters, is not supported, so clearer guidance would be appreciated.
Which solution did I use previously and why did I switch?
We previously used only Snowflake but are now shifting toward a more hybrid architecture.
We primarily added Starburst to our stack due to the potential for significant cost savings and because implementing a lakehouse is a more effective long-term data strategy.
What's my experience with pricing, setup cost, and licensing?
The setup cost is fairly transparent.
There are many opportunities to find cost savings or discounts, especially for a startup like ours.
I appreciate that the pricing is available online, although I will note that comparable compute is only slightly cheaper than Snowflake warehouse costs, for example.
Which other solutions did I evaluate?
We considered Onehouse and Clickhouse as alternative solutions.
What other advice do I have?
We are in the early phases of our Starburst relationship and are looking forward to how we can grow with it in the future.
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?
Amazon Web Services (AWS)
Query federation and consistent SQL interface optimize data integration and analysis
What is our primary use case?
I use Starburst as a cost-efficient hosted option for Trino for data integration and ad-hoc analysis across a broad range of data sources. It is surprisingly useful to query SQL Server, a Google Sheet, data in a blob store, and persist it in Postgres for downstream consumption.
In addition, the Galaxy platform features such as scheduling jobs, offering a data catalog, easy permission and access control management, and the strong technical support from Starburst make it a breeze to use compared to something like Athena.
How has it helped my organization?
I have removed data silos, sped up my pipelines (three to five times the speed of Redshift on a per-cost basis), and now have a single point of entry with consistent SQL semantics to all of my data systems.
What is most valuable?
Query federation coupled with excellent performance is the best feature by far. A consistent interface to all my data systems and a friendly UI that supports data personas from Analyst to Architect and everyone in between is extremely valuable.
What needs improvement?
As a hosted option, I wish I had more control over the cluster configuration, specifically regarding some of the more advanced options. Trino is extremely flexible and powerful, but some of this functionality is gated on the Galaxy platform.
Most users and admins will never need these features, but on occasion I have encountered issues that could have been resolved by a configuration change in five minutes rather than redesigning a data product. That said, I have a high degree of expertise with the tool, and this is more of a quibble than a major issue.
For how long have I used the solution?
I have used the solution for four years.
Which solution did I use previously and why did I switch?
If I have the choice of tooling for managing and interacting with data systems, I always choose Trino first and Starburst Galaxy if I am responsible for managing the deployment. My current team deployed on Redshift before I joined, and the first and best architectural choice I made was to migrate to Galaxy.
What's my experience with pricing, setup cost, and licensing?
You pay for cluster uptime. It is important to be aggressive about autoscaling, as a single worker will get you a long way. I recommend never connecting a BI tool to your Galaxy cluster. Instead, write the data to Postgres or a hot database and serve it from there so you don't pay for expensive uptime to serve dashboards.
Which other solutions did I evaluate?
Having a good amount of expertise in the domain, I knew that Galaxy was the right choice for quick deployment. Having managed data at scale (hundreds of terabytes) in the past, I know Trino will get the job done without a lot of hassle.
Athena specifically has two major issues. First, connectors are restricted on write functionality and are more difficult to configure. Not being able to write through connectors is a deal breaker. Second, if you scale out enough, you will encounter issues due to Athena's shared tenancy model and then need to migrate to Trino eventually. It is better to save yourself the hassle.
What other advice do I have?
If you are unsure about the service, try the free trial. You can be up to speed with your existing systems in half an hour.
Combining organizational data seamlessly with reduced operational costs while creating integrated dashboards
What is our primary use case?
I use Starburst Galaxy to connect to many Amazon S3 and RDS data sources, exposing that data for query and analysis by data engineering teams, as well as executive stakeholders in the organization.
I also use the product to serve many Tableau dashboards used by different teams within the organization.
How has it helped my organization?
I am able to combine data from across the organization to create integrated dashboards that are difficult to construct otherwise.
The on-demand nature of Starburst Galaxy has greatly reduced the computation and operational costs to achieve this compared to other open source tools I have used in the past. With Starburst Galaxy, the data is ready and available 24/7.
What is most valuable?
Starburst Warp Speed has helped me reduce overall operating expenses compared to standard query performance.
I am now able to answer questions in a couple of minutes that would otherwise take hours or days of time for my data engineering teams. I have found the cluster management to be extremely useful. I am able to create clusters configured for various workloads and then turn each one on as needed and let it turn itself off when idle.
This has enabled a number of new use cases. I am able to run much larger jobs than in the past without blocking small concurrent tasks. All of my processes are now running on a cluster that is right-sized instead of trying to manage my own infrastructure by scaling up or down numerous times throughout the day. I am also able to segment costs by product, which I was not able to do in the past.
What needs improvement?
I am able to connect Starburst Galaxy to other tools such as Tableau using the connector, but I would like to see better support for spinning up a cold Starburst Galaxy cluster via Tableau, as it currently just times out.
I would like the Starburst connector in Tableau to have the capability to hold the connection open while Starburst Galaxy starts up.
For how long have I used the solution?
I have used Starburst Galaxy for 1.5 years.
Which solution did I use previously and why did I switch?
I previously used Starburst Enterprise on premise in Amazon Web Services.
I switched to Starburst Galaxy to take advantage of automatic feature upgrades as well as shifting infrastructure costs to Starburst's cloud environment, which operates data workloads more efficiently than the Starburst Enterprise on premise solution.
What's my experience with pricing, setup cost, and licensing?
Pricing for Starburst Galaxy is competitive compared to running my own workloads using open source alternatives.
I recommend you consider the total cost of ownership when deciding whether Starburst Galaxy is a good fit for your organization.
Which other solutions did I evaluate?
I compared Starburst Galaxy to Starburst Enterprise and decided to make the switch to their cloud offering.
What other advice do I have?
This product is worth your time to investigate and evaluate.
I highly recommend Starburst Galaxy to any organization with a need to work with data at scale in the cloud, even in a multi-cloud environment.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
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.
Our team finally gains access to all data in one place
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.
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.
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