Dataiku vs TIBCO Data Science comparison

Dataiku and TIBCO are both solutions in the Data Science Platforms category. Dataiku is ranked #7 with an average rating of 7.5, while TIBCO is ranked #24. Dataiku holds a 12.1% mindshare in Data Science Platforms, compared to TIBCO’s 0.6% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 100% of TIBCO users who would recommend it.

Dataiku

Read 9 Dataiku reviews

7,151 Views
5,935 Comparison Views

100% willing to recommend

TIBCO Data Science

Read 4 TIBCO Data Science reviews

346 Views
279 Comparison Views

100% willing to recommend

Dataiku

TIBCO Data Science

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

TIBCO Data Science and Dataiku are competing products in the data science platform market. Dataiku seems to have the upper hand due to its superior features and integration capabilities.

Features: TIBCO Data Science offers model management, visual analytics, and flexible deployment options, attracting those focused on comprehensive insights and budget constraints. Dataiku excels in collaborative data preparation, extensive integration, and robust support for AI projects, catering to organizations prioritizing teamwork and scalability.

Room for Improvement: TIBCO Data Science could enhance collaboration features, improve integration with third-party tools, and streamline user interface design to increase accessibility. Dataiku might focus on reducing costs, offering more customizable deployment options, and expanding customer support channels to enhance user satisfaction.

Ease of Deployment and Customer Service: TIBCO Data Science provides responsive support with both on-premise and cloud deployment, ideal for tailored solutions. Dataiku emphasizes easy cloud-based deployment with proactive customer service, making it user-friendly and scalable.

Pricing and ROI: TIBCO Data Science presents cost-effective solutions with competitive pricing models, appealing to budget-conscious businesses. Dataiku's higher costs are offset by substantial ROI from enhanced productivity, drawing organizations focused on maximizing returns on their data investments.

To learn more, read our detailed Dataiku vs. TIBCO Data Science Report (Updated: January 2025).

Buyer's Guide

Dataiku vs. TIBCO Data Science

January 2025

Download the complete report

Helped 831,158 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

7th

Average Rating

8.0

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

TIBCO Data Science

Ranking in Data Science Platforms

24th

Average Rating

7.6

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Dataiku is 12.1%, up from 7.8% compared to the previous year. The mindshare of TIBCO Data Science is 0.6%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Sabrine Bendimerad

Data Scientist at Electricite De France

Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub. Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.

Read full review

Veeresh-SHRINGARI

Portfolio Manager, AI & ML at a healthcare company with 10,001+ employees

A straightforward initial setup and good reporting but needs better documentation

It would be ideal if it could be put onto the NMP where you can make more of an analysis. Right now, people don't have enough time to go through the report and make an analysis. It should provide the information of what is on the report into some kind of a dialogue form. Then, a person can ask certain questions and it could interactively give the required report. In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues. If you are doing certain operations of RDBMS, you suffer in terms of the latency of the data. That can be improved upon. Users should be able to cross tables of the web pages they are developing on Spotfire and this needs to be really easy and convenient. Right now, you need to do a lot of tweaks. The solution should be more user-friendly and require fewer tweaks, extensions, and workarounds.

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

"Data Science Studio's data science model is very useful."

"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."

"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."

"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"The most valuable feature is the set of visual data preparation tools."

"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."

"I rate the overall product as eight out of ten."

More Dataiku pros

"We like the way we can drill down into each report to get back data on each project. From the portfolio level, I can see what is happening on it. That is a really important feature. I can look at indirect costs, for example, which are hitting each CIO portfolio. It's good to be able to see actual resources in terms of time as well as cost."

"The most valuable feature is the ease of setting up visualizations."

"The most valuable feature is the performance."

"The idea that you don't have to generate reports each day but they are sent automatically is great."

Cons

"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."

"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."

"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."

"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

"I think it would help if Data Science Studio added some more features and improved the data model."

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

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

"The license is very expensive."

More Dataiku cons

"I would like the visualization for the map of countries to be more easily configurable."

"The scripting for customization could be improved."

"In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues."

"Additional templates would help to get things moving more quickly in terms of getting the reports out."

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

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

831,158 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

No data available

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?

It is very expensive.

See all answers

What needs improvement with Dataiku Data Science Studio?

See all answers

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

We use the solution for data science and machine learning.

See all answers

Ask a question

Earn 20 points

Comparisons

Databricks vs Dataiku

Compared 35% of the time

KNIME vs Dataiku

Compared 16% of the time

Alteryx vs Dataiku

Compared 11% of the time

RapidMiner vs Dataiku

Compared 11% of the time

Amazon SageMaker vs Dataiku

Compared 7% of the time

More Dataiku Competitors

Amazon SageMaker vs TIBCO Data Science

Compared 39% of the time

TIBCO Statistica vs TIBCO Data Science

Compared 37% of the time

More TIBCO Data Science Competitors

Product Reports

Buyer's Guide

Dataiku

January 2025

Download Dataiku product report

Buyer's Guide

Data Science Platforms

January 2025

Download TIBCO Data Science product report

Also Known As

Dataiku DSS

Alpine Data Chorus

Learn More

Dataiku

TIBCO

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.

TIBCO Spotfire Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.

Sample Customers

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

Havas Media, Tipping Point Community, eviCore

Buyer's Guide

Dataiku vs. TIBCO Data Science

January 2025

Free Report: Dataiku vs. TIBCO Data Science

Find out what your peers are saying about Dataiku vs. TIBCO Data Science and other solutions. Updated: January 2025.

DOWNLOAD NOW

831,158 professionals have used our research since 2012.

See our Dataiku vs. TIBCO Data Science 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.