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Dataiku vs Starburst Enterprise comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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
7th
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Starburst Enterprise
Ranking in Data Science Platforms
14th
Average Rating
8.6
Reviews Sentiment
6.9
Number of Reviews
2
Ranking in other categories
Streaming Analytics (12th)
 

Mindshare comparison

As of March 2025, in the Data Science Platforms category, the mindshare of Dataiku is 12.5%, up from 8.0% compared to the previous year. The mindshare of Starburst Enterprise is 2.2%, up from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

RichardXu - PeerSpot reviewer
The platform organizes workflows visually and efficiently
One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.
KamleshPant - PeerSpot reviewer
Connects to any data source from any region and offers unified access
There are no specific projects supported by Starburst regarding AI initiatives or machine learning projects. In the future, if we have all the data available, we can definitely capitalize on AI/ML and LLM capabilities to summarize data and gain insights. That's our future goal, but we haven't reached that point yet. There should be support for REST API data sources to access data from the web. We often have data coming in and communicate with data sources via REST API calls. I don't see that capability in Starburst currently; everything is through JDBC or ODBC. If Starburst could seamlessly access data using REST API capabilities, it would be a game-changer. The self-service data management features, like self-service materialized views, are great, but they can be a bit complex for basic users to understand.

Quotes from Members

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

Pros

"Data Science Studio's data science model is very useful."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."
"I rate the overall product as eight out of ten."
"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"The most valuable feature is the set of visual data preparation tools."
"One of the valuable features of Dataiku is the workflow capability."
"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."
"It's very scalable, fast performing, and supports many catalogs."
 

Cons

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"One area for improvement is the need for more capabilities similar to those provided by NVIDIA for parallel machine learning training. We still encounter some integration issues."
"The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests."
"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."
"The ability to have charts right from the explorer would be an improvement."
"The license is very expensive."
"We still encounter some integration issues."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"There should be support for REST API data sources to access data from the web."
"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."
 

Pricing and Cost Advice

"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."
"Pricing is pretty steep. Dataiku is also not that cheap."
"I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of using Starburst Enterprise can vary based on the amount of data you're processing and the type of machines you opt for, whether on AWS or another cloud platform."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Educational Organization
14%
Manufacturing Company
9%
Computer Software Company
8%
Financial Services Firm
45%
Computer Software Company
10%
Energy/Utilities Company
5%
Government
4%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?
The pricing for Dataiku is very high, which is its biggest downside. The model they follow is not consumption-based, making it expensive.
What needs improvement with Dataiku Data Science Studio?
Dataiku's pricing is very high, and commercial transparency is a challenge. Support is also an area needing improvement. More features like LLM security, holographic encryption, and enhanced GPU in...
What is your primary use case for Dataiku Data Science Studio?
My primary use case for Dataiku ( /products/dataiku-reviews ) is for data science, Gen ( /products/gen-reviews ) AI, and data science applications. Our AGN team also uses it for various purposes.
What is your experience regarding pricing and costs for Starburst Enterprise?
I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of us...
What needs improvement with Starburst Enterprise?
There are no specific projects supported by Starburst regarding AI initiatives or machine learning projects. In the future, if we have all the data available, we can definitely capitalize on AI/ML ...
What is your primary use case for Starburst Enterprise?
We use Starburst with one client who is exploring their ecosystem to remove data silos and enable data access across departments. It's a very big ecosystem, like a finance institute. They are curre...
 

Comparisons

 

Also Known As

Dataiku DSS
No data available
 

Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
Information Not Available
Find out what your peers are saying about Dataiku vs. Starburst Enterprise and other solutions. Updated: March 2025.
842,388 professionals have used our research since 2012.