Darwin vs Starburst Galaxy comparison

Why PeerSpot?

SparkCognition and Starburst are both solutions in the Data Science Platforms category. SparkCognition is ranked #23, while Starburst is ranked #7 with an average rating of 9.3. SparkCognition holds a 1.5% mindshare in Data Science Platforms, compared to Starburst’s 1.5% 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

1,706 Views
1,706 Comparison Views

100% willing to recommend

Starburst Galaxy

Read 12 Starburst Galaxy reviews

2,242 Views
1,726 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: September 2026).

Buyer's Guide

Darwin vs. Starburst Galaxy

September 2026

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Helped 916,771 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

Darwin

Ranking in Data Science Platforms

23rd

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

7th

Average Rating

9.4

Reviews Sentiment

2.6

Number of Reviews

Ranking in other categories

Streaming Analytics (8th)

Mindshare comparison

As of October 2026, in the Data Science Platforms category, the mindshare of Darwin is 1.5%, up from 0.5% compared to the previous year. The mindshare of Starburst Galaxy is 1.5%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Starburst Galaxy	1.5%
Darwin	1.5%
Other	97.0%

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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Pedromachado Ventura

Data Analyst at a financial services firm with 5,001-10,000 employees

Unified SQL layer has streamlined access to distributed historical data for analytics and reporting

One area that I think could be improved is the experience when performance issues occur. When a query is slow, it is not always immediately obvious to me whether the bottleneck comes from Starburst Galaxy itself, the underlying data source, the query design, or the reporting tool. Better visibility into query performance and easier diagnostics for non-administrators would be useful. Another potential improvement would be enhancing the experience with BI tools to make it more seamless. I work a lot with Power BI, and when you are working with larger data sets, performance can sometimes depend on several different layers. Having more visibility into what is happening between the BI tool, Starburst Galaxy, and the underlying source would be helpful. I also think onboarding could be a little more accessible for analysts. There is good technical documentation, but sometimes I just need to understand the best way to approach a common use case without diving too deep into the platform architecture. The main improvement would be troubleshooting. I have not used the AI capabilities extensively, so I cannot give a detailed assessment. I am not sure if my organization has the full capabilities of Starburst Galaxy, but I think adding AI on top of the data layer is interesting, especially if it can help users discover data, understand data sets, and interact with them more naturally. For governance and security, one of the strengths of Starburst Galaxy is that you can centralize access to data while still controlling what different users are allowed to see. Role-based access, fine-grained permissions, and data masking are important because giving people easier access to data should not mean giving everyone access to everything. I think that is even more important than any AI capabilities that are introduced. If you do introduce AI, I think it should respect exactly the same data permissions and governance rules as the user that is accessing the data.

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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

"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."

"The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."

"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."

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

"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."

"Our main goal is to transform data into knowledge and Darwin is definitely helping us to do that faster."

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

"Darwin is really useful for people who don't necessarily do a lot of data science."

More Darwin pros

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

"The main positive impact Starburst Galaxy has made is making data more accessible for analytics and reporting."

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

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

"Starburst Galaxy serves the best purpose for me because if my SLAs are not met with my customers, they will raise a case, and I have tried many other tools, but Starburst Galaxy fits the best."

More Starburst Galaxy pros

Cons

"Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."

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

"If you give a lot of data to Darwin, sometimes it can hang."

"Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model."

"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."

"The analyze function takes a lot of time."

More Darwin cons

"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."

"I would like to see better alerting integrations for failures and errors in scheduled tasks and maintenance jobs."

"I think there are areas of improvement with respect to AI adaptability, and also in general, the amount of connectors working with other tools are areas where it can be expanded."

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

"Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose."

"There is still room for improved usability and diagnostics, especially for users who are not platform specialists."

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

"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."

More Starburst Galaxy cons

Pricing and Cost Advice

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

"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."

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

"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."

Information not available

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Top Industries

By visitors reading reviews

Construction Company

16%

Manufacturing Company

12%

Financial Services Firm

12%

Outsourcing Company

11%

Financial Services Firm

30%

Computer Software Company

10%

Manufacturing Company

Construction Company

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	4

Questions from the Community

Ask a question

Earn 20 points

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.

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What needs improvement with Starburst Galaxy?

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What is your primary use case for Starburst Galaxy?

I have mainly used Starburst Galaxy as a query and access layer for analytical data, as my current work involves dealing with data stored across different platforms, including Hive and SingleStore....

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Product Reports

Buyer's Guide

Darwin

October 2026

Download Darwin product report

Buyer's Guide

Starburst Galaxy

September 2026

Download Starburst Galaxy product report

Overview

Darwin offers advanced features like automated model-building, data cleaning, and rapid iteration, designed for efficient and intuitive use, enhancing productivity through easy system integration and model optimization.

Darwin caters to enterprises needing robust data management and streamlined model development. It provides tools for evaluating dataset quality and resolving data issues such as missing entries or incorrect types. With its REST API, it integrates seamlessly into existing systems, empowering rapid model optimization. While users find its interface intuitive, there is a demand for more advanced functionalities such as direct data access through APIs and enhancements in non-supervised models. The platform's educational resources and transparency in operations are areas identified for further improvement, along with user-friendly enhancements to dashboards.

What are Darwin's Most Important Features?

Data Cleaning: Ensures datasets are accurate by handling missing or incorrect entries.
Automated Model-Building: Streamlines the creation of predictive models.
Rapid Model Iteration: Allows for quick adjustments and improvements.
Intuitive UI: Simplifies user interactions.
REST API: Facilitates seamless integration with current systems.

What Benefits or ROI Can Users Expect?

Enhanced Productivity: Saves time with automation and integration capabilities.
Industry-Specific Solutions: Partner network supports tailored approaches.
Risk Management: Aids in predicting credit defaults and risk evaluation.
Operational Efficiency: Reduces reliance on data scientists through smarter automation.

Darwin is instrumental in industries like lending, where it's used for predicting credit defaults and managing risk portfolios. It supports client segmentation and delinquency assessment, allowing firms to analyze data for preemptive actions. Additionally, it's effective in sectors such as oil, gas, and aerospace for data analysis, supply chain optimization, and model creation, promoting efficient processes and reducing dependence on specialist skills.

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

September 2026

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