Dremio vs Starburst Galaxy comparison

Dremio and Starburst are both solutions in the Data Science Platforms category. Dremio is ranked #11 with an average rating of 8.7, while Starburst is ranked #10 with an average rating of 9.8. Dremio holds a 2.3% mindshare in Data Science Platforms, compared to Starburst’s 1.0% mindshare. Additionally, 100% of Dremio users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Dremio

Read 11 Dremio reviews

3,361 Views
1,983 Comparison Views

100% willing to recommend

Starburst Galaxy

Read 9 Starburst Galaxy reviews

789 Views
647 Comparison Views

100% willing to recommend

Dremio

Starburst Galaxy

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Nov 9, 2025

Dremio and Starburst Galaxy are prominent in the data management and analytics category. While Dremio offers comprehensive features, Starburst often takes an upper hand in query speed and flexibility.

Features: Dremio is recognized for its integration with Apache Airflow, creating services without extra resources, and compatibility with Amazon S3 and Azure. It supports efficient change management and data lineage via Nessie and enhances data operations using Arrow. The ease of querying and creating virtual datasets also makes it stand out. Starburst Galaxy boasts a fast query engine, Iceberg support, and adaptability to various warehouses. It simplifies data integration workflows and provides robust cluster management, particularly excelling in federated querying.

Room for Improvement: Dremio's challenges include Delta connector support, large query execution time, and integration issues with tools like Databricks. Performance concerns and high licensing costs are also areas needing attention. Starburst Galaxy faces issues with cluster spin-up times and lacks support for some emerging dbt features. Although proficient in Tableau integration, improvements in AI usability, query monitoring, and documentation are desired.

Ease of Deployment and Customer Service: Dremio offers hybrid and on-premises deployment but faces support challenges due to staff shortages. Complex queries often require developer intervention, although customer service is responsive. Starburst Galaxy centers on public cloud deployments, offering a straightforward support and deployment process, ensuring faster onboarding, though documentation could be improved to better meet user needs.

Pricing and ROI: Dremio delivers a cost-effective model by reducing manpower and infrastructure costs despite expensive licensing affecting scalability. Starburst Galaxy provides transparent pricing, competitive with open-source alternatives and Snowflake, emphasizing total cost of ownership benefits. Its predictable pricing and flexible cluster management align with cost benefits, highlighting the importance of evaluating specific use cases to maximize value.

To learn more, read our detailed Dremio vs. Starburst Galaxy Report (Updated: December 2025).

Buyer's Guide

Dremio vs. Starburst Galaxy

December 2025

Download the complete report

Helped 881,082 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Dremio

Ranking in Data Science Platforms

11th

Average Rating

8.4

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (5th)

Starburst Galaxy

Ranking in Data Science Platforms

10th

Average Rating

9.8

Reviews Sentiment

1.0

Number of Reviews

Ranking in other categories

Streaming Analytics (13th)

Mindshare comparison

As of January 2026, in the Data Science Platforms category, the mindshare of Dremio is 2.3%, down from 4.2% compared to the previous year. The mindshare of Starburst Galaxy is 1.0%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Starburst Galaxy	1.0%
Dremio	2.3%
Other	96.7%

Data Science Platforms

Featured Reviews

Corrr Moray

SR BI developer at BRQ Digital Solutions

Has simplified complex data integration workflows and supported consistent reporting across multiple sources

We also have a close relationship with the team that does the Dremio maintenance for the database, like upgrading the versions and they know about some specific problems we had in the past, such as a memory leak. We had a memory leak on some versions, which sometimes stopped the service. Since we are using Dremio installed like a server, not a SaaS solution, many times we need to stop and restart the service to clear all the cache and all that, and this is the thing I should add. I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement. I remember using some features in the past, like pivot tables, which proved to be really difficult, but I know this is a fault also for other vendors. Pivoting, transposing, and unpivoting are often not so good. CTEs also many times prove to be not so good, so I think these two main items could be improved significantly if they standardize them.

Read full review

reviewer2750097

VP, Business Intelligence at a outsourcing company with 501-1,000 employees

Unified data access improves analytics and simplifies complex processes

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Dremio allows querying the files I have on my block storage or object storage."

"The first feature that stands out for me in Dremio is the federated type of query, which allows the possibility to use multiple endpoints without worrying about writing custom SQL that runs only for SQL Server or for Postgres and Redshift."

"Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me."

"Dremio gives you the ability to create services which do not require additional resources and sterilization."

"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."

"Overall, you can rate it as eight out of ten."

"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."

"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."

More Dremio pros

"Starburst Galaxy is becoming a cornerstone of our data platform, empowering us to make smarter and faster decisions across the organization."

"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."

"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."

"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."

"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."

"Starburst has provided us with virtually guaranteed performance on complex queries across datasets that are in the tens of gigabytes which complete in seconds."

"Starburst Galaxy has significantly improved our data architecture flexibility and performance management by solving cross-database query challenges and enabling us to utilize iceberg tables externally across our entire data ecosystem."

"Starburst has provided us with virtually guaranteed performance on complex queries across datasets that are in the tens of gigabytes which complete in seconds."

More Starburst Galaxy pros

Cons

"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

"We had a memory leak on some versions, which sometimes stopped the service."

"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."

"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."

"They need to have multiple connectors."

"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."

"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."

"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."

More Dremio cons

"The most persistent issue is the cluster spin-up time."

"I would like Starburst to leverage AI to improve usability. Data lakes are complicated and difficult for users to explore."

"Cluster startup time can be slow, sometimes taking over a minute."

"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."

"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."

Pricing and Cost Advice

"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."

"Dremio is less costly competitively to Snowflake or any other tool."

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

881,082 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

28%

Computer Software Company

Manufacturing Company

Healthcare Company

Financial Services Firm

26%

Computer Software Company

14%

Government

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	5
Large Enterprise	5

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	1

Questions from the Community

What do you like most about Dremio?

Dremio allows querying the files I have on my block storage or object storage.

See all answers

What is your experience regarding pricing and costs for Dremio?

I don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.

See all answers

What needs improvement with Dremio?

I wouldn't say there is anything Dremio can be improved on. If I could change something, I would say many developers and programmers, when they are starting to work in this specific field or area, ...

See all answers

What is your experience regarding pricing and costs for Starburst Galaxy?

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

See all answers

What needs improvement with Starburst Galaxy?

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 ...

See all answers

What is your primary use case for Starburst Galaxy?

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.

See all answers

Comparisons

Databricks vs Dremio

Compared 33% of the time

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 6% of the time

Dataiku vs Dremio

Compared 6% of the time

Microsoft Power BI vs Dremio

Compared 3% of the time

More Dremio Competitors

Starburst Enterprise vs Starburst Galaxy

Compared 35% of the time

Databricks vs Starburst Galaxy

Compared 18% of the time

Qlik Talend Cloud vs Starburst Galaxy

Compared 10% of the time

Dataiku vs Starburst Galaxy

Compared 9% of the time

IBM Watson Studio vs Starburst Galaxy

Compared 8% of the time

More Starburst Galaxy Competitors

Product Reports

Buyer's Guide

Dremio

January 2026

Download Dremio product report

Buyer's Guide

Starburst Galaxy

January 2026

Download Starburst Galaxy product report

Also Known As

Dremio AWS - BYOL

No data available

Overview

Dremio offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.

Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.

What are Dremio's most valuable features?

Native Error Interfaces: Simplifies error detection and troubleshooting
Integration with Apache Airflow: Enhances workflow automation capabilities
Scalability: Effortlessly handles growing data demands
Role-Based Access Management: Provides secure data access control
Data Reflection: Optimizes query performance automatically
Query Federation: Supports queries across multiple endpoints seamlessly
Data Cataloging and Virtualization: Offers streamlined data organization

What benefits and ROI should users look for?

Reduced Data Storage Costs: Efficiently manages data to minimize storage expenses
Improved Query Performance: Enhances processing speed for large datasets
Flexibility in Integration: Easily connects with storage solutions like Oracle and MySQL
Enhanced Data Management: Supports comprehensive data analysis workflows

Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.

Dremio

Starburst Galaxy offers rapid query speeds and robust cluster management, enhancing data engineering efficiency while supporting AWS integrations and cross-database functionality. Users benefit from its advanced data integration and federated querying capabilities.

Starburst Galaxy stands out with a compute-focused architecture that excels in facilitating seamless data integration. Technological innovations like autoscaling clusters and automated metadata management optimize operations in multi-tenant environments. With a keen emphasis on compatibility, the platform provides support for AWS Glue and enables federated querying across S3, Snowflake, and Redshift. This adaptability ensures comprehensive ETL processes and enhances analytics through querying SQL Server, Google Sheets, and blob stores. While noted for its robust capabilities, users seek improvements in cluster startup times, Tableau and AI support, and desire infrastructure-as-code enhancements.

What are Starburst Galaxy's key features?

Rapid Query Speeds: Enhances data processing with quick responses.
Cluster Autoscaling: Optimizes resource allocation for varying workloads.
Federated Querying: Cross-database functionality for seamless integration.
Compute-Focused Architecture: Ensures efficiency and scalability.
Automated Metadata Management: Streamlines data handling processes.
AWS Integration: Broad compatibility with AWS services.

What benefits should users evaluate in reviews?

Data Engineering Efficiency: Reduces operating expenses and improves productivity.
Cost Reduction: Highlights lower storage costs with efficient data handling.
Seamless Integration: Enables robust data integration across various platforms.
Cluster Management: Supports diverse workloads with advanced management tools.

In industries focusing on large-scale data efforts, Starburst Galaxy plays an essential role in connecting data sources like Amazon S3 and RDS, streamlining tasks in data engineering and ad-hoc analysis across complex environments. Teams leverage its cross-database querying to boost AWS analytics, with features tailored for sectors needing agile data solutions, from ETL pipelines to secure data federation.

Starburst

Sample Customers

UBS, TransUnion, Quantium, Daimler, OVH

Information Not Available

Buyer's Guide

Dremio vs. Starburst Galaxy

December 2025

Free Report: Dremio vs. Starburst Galaxy

Find out what your peers are saying about Dremio vs. Starburst Galaxy and other solutions. Updated: December 2025.

DOWNLOAD NOW

881,082 professionals have used our research since 2012.

See our Dremio vs. Starburst Galaxy report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.