Starburst Galaxy

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.

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.

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 have used the solution for 1.5 years.

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.

Public Cloud

Amazon Web Services (AWS)

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.

Public Cloud

Amazon Web Services (AWS)

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.

Public Cloud

Amazon Web Services (AWS)