Dataiku vs Starburst Galaxy comparison

Why PeerSpot?

Dataiku and Starburst are both solutions in the Data Science Platforms category. Dataiku is ranked #2 with an average rating of 8.3, while Starburst is ranked #7 with an average rating of 9.3. Dataiku holds a 4.4% mindshare in Data Science Platforms, compared to Starburst’s 1.5% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Dataiku

Read 21 Dataiku reviews

7,886 Views
7,728 Comparison Views

100% willing to recommend

Starburst Galaxy

Read 12 Starburst Galaxy reviews

2,242 Views
1,726 Comparison Views

100% willing to recommend

Dataiku

Starburst Galaxy

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Dataiku 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 Dataiku vs. Starburst Galaxy Report (Updated: September 2026).

Buyer's Guide

Dataiku vs. Starburst Galaxy

September 2026

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Helped 917,433 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

Dataiku

Ranking in Data Science Platforms

2nd

Average Rating

8.2

Reviews Sentiment

6.5

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 Dataiku is 4.4%, down from 11.7% 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 (%)
Dataiku	4.4%
Starburst Galaxy	1.5%
Other	94.1%

Data Science Platforms

Featured Reviews

Shubhamkumar Gomar

Senior Data Scientist at Deloitte

Visual workflows have streamlined healthcare analytics and have reduced reporting time significantly

In terms of improvement, I cannot comment on the LLMs or the agentic view as I have not used them yet. However, I feel that better documentation is necessary. Dataiku should establish a stronger community since this is proprietary software, where users can share knowledge. Although they have some community interaction, it is often challenging to find assistance when stuck. For example, when I was new to Dataiku and trying to use an external optimization tool such as CPLEX, I struggled with resource directory linking to a project's notebook. Detailed documentation and community discussions could have significantly alleviated these issues for users such as myself.

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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.

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

"Dataiku is a complete platform to build ETL and data pipeline and deploy it, which I appreciate."

"I like the interface, which is probably my favorite part of the solution; it is really user-friendly, colorful, and I think it is really beautiful and well-designed."

"Compared to Informatica, this tool is extremely easy with its GUI-based functionality and large compatibility with various data sources, and maintenance processes are much more automated than ever, with fewer errors."

"Dataiku is highly regarded as it is a leader in the Gartner ranking."

"Technical support is really, really good."

"One of the valuable features of Dataiku is the workflow capability."

"Dataiku has positively impacted my organization since we use it majorly for our day-to-day work, and it is very helpful in creating and managing ETL pipelines to create a project flow, making it easy to go back to any step and then make edits if some changes occur."

"I consider the return on investment with Dataiku valuable because for us, it is one single platform where all our data scientists come together and work on any model building, so it is collaboration, plus having everything in one place, organized, having proper project management, and then built-in capabilities which help to facilitate model building."

More Dataiku 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 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."

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

"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."

"Starburst Galaxy has positively impacted my organization by allowing us to rethink the strategy for data and architect data differently; instead of having multiple data marts and siloed data marts, we have a unified vision, and that is how it is changing."

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

More Starburst Galaxy pros

Cons

"I find that it is a little slow during use. When I use Dataiku to run my script to transfer data, it takes more time than I would expect for the operation to complete."

"In terms of enhancing collaboration within my team, I would not say Dataiku is the best one because it's so expensive."

"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."

"However, I feel that better documentation is necessary."

"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."

"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."

"Dataiku's scalability is not one of the best solutions to scale."

More Dataiku cons

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

"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."

"As a hosted option, I wish I had more control over the cluster configuration, specifically regarding some of the more advanced options."

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

"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."

"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."

More Starburst Galaxy cons

Pricing and Cost Advice

"Pricing is pretty steep. Dataiku is also not that cheap."

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

10%

Computer Software Company

10%

Comms Service Provider

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
Midsize Enterprise	2
Large Enterprise	13

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

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?

The licenses are a bit high for companies that are still hesitating to get started with using Dataiku. For my personal projects, I used the thirty-day free trial. Regarding my company, I did not ha...

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What needs improvement with Dataiku Data Science Studio?

I have no suggestions for improvements because it's all good; it just sometimes lags a lot, and I don't know if the server is full or what, but it sometimes takes a lot of time while loading and re...

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What is your primary use case for Dataiku Data Science Studio?

My main use case for Dataiku involves ETL pipelines, mainly for data analysis, and I majorly use SQL queries for that. For ETL pipelines and data analysis, I had to create the output by combining a...

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?

See all answers

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....

See all answers

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Buyer's Guide

Dataiku

October 2026

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Buyer's Guide

Starburst Galaxy

September 2026

Download Starburst Galaxy product report

Also Known As

Dataiku DSS

No data available

Overview

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

Dataiku

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

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

Information Not Available

Buyer's Guide

Dataiku vs. Starburst Galaxy

September 2026

Free Report: Dataiku vs. Starburst Galaxy

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