Darwin vs Starburst Galaxy comparison

SparkCognition and Starburst are both solutions in the Data Science Platforms category. SparkCognition is ranked #25, while Starburst is ranked #9 with an average rating of 9.8. SparkCognition holds a 1.5% mindshare in Data Science Platforms, compared to Starburst’s 1.2% mindshare. Additionally, 100% of SparkCognition users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Darwin

Read 8 Darwin reviews

844 Views
836 Comparison Views

100% willing to recommend

Starburst Galaxy

Read 9 Starburst Galaxy reviews

1,011 Views
809 Comparison Views

100% willing to recommend

Darwin

Starburst Galaxy

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Darwin and Starburst Galaxy based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Darwin vs. Starburst Galaxy Report (Updated: March 2026).

Buyer's Guide

Darwin vs. Starburst Galaxy

March 2026

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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

Darwin

Ranking in Data Science Platforms

25th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Starburst Galaxy

Ranking in Data Science Platforms

9th

Average Rating

9.8

Reviews Sentiment

1.0

Number of Reviews

Ranking in other categories

Streaming Analytics (12th)

Mindshare comparison

As of March 2026, in the Data Science Platforms category, the mindshare of Darwin is 1.5%, up from 0.3% compared to the previous year. The mindshare of Starburst Galaxy is 1.2%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Starburst Galaxy	1.2%
Darwin	1.5%
Other	97.3%

Data Science Platforms

Featured Reviews

AaronCooke

Founder at Helio Summit

Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.

There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.

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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.

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Quotes from Members

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

Pros

"The thing that I find most valuable is the ability to clean the data."

"Due to the predictions that we have been able to do because of the use of Darwin, we have decreased our delinquency index from almost nine percent to five percent and reduced our client loss index from 19 percent to 10 percent."

"It did help us to convert data into knowledge."

"We have managed to reduce the percentage of high-risk clients from 8.9 percent to 5 percent, which is very significant in the overall quality of our clients and very good news for everyone, especially our investors."

"In terms of streamlining a lot of the low-level data science work, it does a few things there."

"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."

"Even people who are not fully technical can use it with a little guidance or something."

"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."

More Darwin pros

"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 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."

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

"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 use Starburst as a cost-efficient hosted option for Trino for data integration and ad-hoc analysis across a broad range of data sources."

"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."

More Starburst Galaxy pros

Cons

"The license cost is not cheap, especially not for markets like Mexico."

"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."

"Darwin is stable, but it just doesn't provide the functionality that an analyst would need."

"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."

"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin."

"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."

"We have used Darwin as a complement to other tools like R and SPSS to get the accuracy we want."

"In the beginning, when we started to see how Darwin works, we thought that maybe, from raw, dirty data, we could generate a model really fast, but that's not true."

More Darwin cons

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

"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."

"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."

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

Pricing and Cost Advice

"In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."

"As far as I understand, my company is not paying anything to use the product."

"I believe our cost is $1,000 per month."

"The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."

Information not available

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

24%

Computer Software Company

14%

University

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Large Enterprise	2

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

Questions from the Community

Ask a question

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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

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Saturn Cloud vs Darwin

Compared 4% of the time

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Compared 28% of the time

Databricks vs Starburst Galaxy

Compared 11% of the time

Qlik Talend Cloud vs Starburst Galaxy

Compared 9% of the time

Dremio vs Starburst Galaxy

Compared 9% of the time

Altair Knowledge Studio vs Starburst Galaxy

Compared 3% of the time

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Product Reports

Buyer's Guide

Darwin

March 2026

Download Darwin product report

Buyer's Guide

Starburst Galaxy

February 2026

Download Starburst Galaxy product report

Overview

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

SparkCognition

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

Hunt Oil, Hitachi High-Tech Solutions

Information Not Available

Buyer's Guide

Darwin vs. Starburst Galaxy

March 2026

Free Report: Darwin vs. Starburst Galaxy

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