Starburst Galaxy

I use Starburst Galaxy  on AWS  as a federated query engine to access our S3-based Iceberg data lake, Snowflake , and Redshift without duplicating data. This enables secure, high-performance analytics and machine learning workloads with consistent governance across all data sources.

Starburst Galaxy  has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes. In addition to our original use case, it has proven fast and reliable for Iceberg table maintenance, and it has enabled ingestion of Kafka feeds into our AWS  S3  data lake, further increasing its value to our data platform.

The features I value most are federated querying across S3  Iceberg, Snowflake , and Redshift; native Iceberg table management tools that make maintenance operations simple and performant; and the ability to connect directly to Kafka for streaming ingestion. The federated query capability has also enabled me to build a Sigma  Computing dashboard that pulls data from Postgres, BigQuery , and Snowflake through a single Starburst Galaxy connection, greatly simplifying data access and integration.

I would like to see better alerting integrations for failures and errors in scheduled tasks and maintenance jobs. I also want support for more connectors such as Kinesis  and Firehose, support for more file types such as Avro and JSON, and object storage message queue integration for object storage integrations. A single view of query execution and optimization details, rather than needing to toggle between the Galaxy  and Trino UI, would be helpful. Additionally, enhanced control over account and environment variables that would be available in the Enterprise edition would be beneficial.

I previously used several query engines, including Athena , EMR, Redshift, Snowflake, and BigQuery . Starburst Galaxy’s federated query capabilities allowed me to join data across clouds and platforms, reducing complexity.

I recommend tracking usage metrics from the start, focusing on data scanned and query concurrency, so you can right-size spend. If workloads are steady, you should explore commitment-based pricing for better rates and factor in the operational savings from not having to manage and scale your own Trino or query infrastructure.

I reviewed several options including Databricks  and Dremio . I was an early adopter of Snowflake and still use it as well. Starburst Galaxy was a better fit for my technology stack and developers.

I have found that Starburst Galaxy’s flexibility makes it worth experimenting beyond the initial deployment plan. Features I originally viewed as secondary, such as Iceberg maintenance and Kafka ingestion, have become everyday tools. Building a strong relationship with the Starburst team has also helped me optimize configurations and discover new capabilities faster.

Starburst Galaxy  serves as our primary SQL-based data processing engine, a strategic decision driven by its seamless integration with our AWS  cloud infrastructure and its ability to deliver high performance with low-latency responses.

The platform provides a comprehensive suite of functionalities that significantly enhance the daily operations of our data engineers and data analysts.

Starburst Galaxy  has been instrumental in reducing the maintenance effort and management overhead of our Trino cluster, which is particularly valuable given our lean platform team responsible for Kovi's data infrastructure.

The platform has enabled us to deploy multiple clusters for different purposes while providing clear cost tracking and utilization monitoring capabilities.

The most relevant functionalities today are cluster autoscaling for intensive load periods and automated metadata management through cleaning, compression, and orphaned file deletion in Iceberg.

These capabilities significantly reduce reading costs, storage expenses, and query processing overhead.

I maintain weekly conversations with Starburst's development and support teams, which provides me with visibility into the product roadmap and evolution.

Currently, my primary need is the impersonation functionality for BI solutions within Starburst clusters, which would enable enhanced access control and data governance capabilities.

I have used the solution for almost 2 years.

Previously, I utilized the AWS  stack with Redshift and Athena .

I chose to migrate to Starburst Galaxy due to their expertise with Trino, superior aggregate cost structure compared to my previous solutions, and the rapid product evolution with new functionalities, problem corrections, and performance improvements.

Since Starburst Galaxy's pricing model is simple to understand and easy to predict, there are no major secrets.

Everything is transparent and accessible through the product console.

The only point of attention is the S3  and transfer costs that should also be included when calculating the total cost.

I use the solution for processing large simulation datasets into aggregated datasets that can either be used for real-time data analysis or stored for later analysis.

Starburst has provided us with virtually guaranteed performance on complex queries across datasets that are in the tens of gigabytes which complete in seconds. This allows me to concentrate on the features I want to deliver to our end users rather than diagnosing performance issues.

The most valuable features include taking care of the minutiae of Trino management so that it is well-optimized for our use case out of the box. Additionally, the ability to write to Apache Iceberg tables enables complex queries to be written to S3 , avoiding the need for them to be re-run repeatedly.

I also find attribute-based access control valuable, as it allows end users to access only their data in a multi-tenant environment.

Multi-tenancy could be improved. In order to have multiple environments for SSO , we maintain multiple tenants that are connected to different AWS  accounts via the Marketplace. On the AWS  side this setup works because all accounts belong to the same organization. However, on the Starburst side these tenants are disconnected from each other, and it would be great if they could be connected and managed centrally.

I previously used Amazon Athena . I switched because the performance offered by Starburst was significantly better than that provided by Athena . Additionally, Starburst allowed for integrations with BI tools, which was difficult to achieve with the necessary level of security in Athena .

I recommend experimenting with different cluster sizes to determine what works best for your particular use case.

I considered Amazon Athena  and Firebolt  as alternative solutions.

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 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.

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.

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.

We have used Starburst Galaxy for a few months.

We migrated our self-hosted Trino instance to Starburst Galaxy.

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.

We briefly explored other options, but given the one-to-one nature of Trino and Starburst Galaxy, it made for a more seamless transition.

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.

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 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.

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.

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.

I have used the solution for over three years.

I used other tools before, and I switched because Starburst is faster.

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.

I evaluated other solutions such as Databricks .

I wish there were more products available in the ecosystem.