Dataiku vs IBM SPSS Modeler vs KNIME Business Hub comparison

Dataiku and IBM are both solutions in the Data Science Platforms category. Dataiku is ranked #6 with an average rating of 8.2, while IBM is ranked #19 with an average rating of 7.0. Dataiku holds a 12.9% mindshare in Data Science Platforms, compared to IBM’s 2.6% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 86% of IBM users who would recommend it.

Dataiku

Read 12 Dataiku reviews

6,233 Views
6,046 Comparison Views

100% willing to recommend

IBM SPSS Modeler

Read 40 IBM SPSS Modeler reviews

2,195 Views
1,449 Comparison Views

86% willing to recommend

KNIME Business Hub

Read 60 KNIME Business Hub reviews

10,110 Views
7,077 Comparison Views

94% willing to recommend

Dataiku

IBM SPSS Modeler

KNIME Business Hub

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Dataiku, IBM SPSS Modeler, and KNIME Business Hub based on real PeerSpot user reviews.

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To learn more, read our detailed Data Science Platforms Report (Updated: August 2025).

Buyer's Guide

Data Science Platforms

August 2025

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Helped 866,088 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:

Mindshare comparison

As of August 2025, in the Data Science Platforms category, the mindshare of Dataiku is 12.9%, up from 9.7% compared to the previous year. The mindshare of IBM SPSS Modeler is 2.6%, up from 2.5% compared to the previous year. The mindshare of KNIME Business Hub is 11.9%, up from 10.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
KNIME Business Hub	11.9%
Dataiku	12.9%
IBM SPSS Modeler	2.6%
Other	72.6%

Data Science Platforms

Featured Reviews

RichardXu

Data Science Lead at a mining and metals company with 10,001+ employees

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.

Read full review

PeterHuo

Application Architect at Canada Border Services Agency

Good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data

There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database. When streams become larger, performance bottlenecks may occur in the IBM SPSS Modeler server or the database. Sometimes the server crashes and needs to be restarted to release memory on both sides. I'm not sure exactly where the problem is caused, as I focus on stream design rather than server issues. The problem could be on the IBM SPSS Modeler server and database.

Read full review

Laurence Moseley

Emeritus Professor of Health Services Research at University of South Wales

Has a drag-and-drop interface and AI capabilities

It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data. Next, you can clean the data by handling missing values, selecting specific columns you want to analyze, and then proceeding with your analysis, such as regression or correlation. KNIME has over 4,500 nodes available for download. In addition, KNIME offers various extensions. For instance, the text processing extension allows you to process text data efficiently. It's more powerful than other tools like NVivo and Palantir. KNIME also has AI capabilities. If you're unsure about the next step, the AI assistant can suggest the most frequently used nodes based on your previous work. Another valuable feature is the integration with Python. If you need to perform a task that requires Python, you can simply add a Python node, write the necessary code,

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

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

"I believe the return on investment looks positive."

"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."

"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."

"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."

"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."

"Our clients can easily drag and drop components and use them on the spot."

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

More Dataiku pros

"It gives you a GUI interface, which is a lot more user-friendly and easier to use compared to writing R scripts or Python."

"It is very scalable for non-technical people."

"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."

"We have full control of the data handling process."

"We are using it either for workforce deployment or to improve our operations."

"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."

"It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"

"It works fine. I have not had any stability issues; it is always up."

More IBM SPSS Modeler pros

"It's a coding-less opportunity to use AI. This is the major value for me."

"Overall KNIME serves its purpose and does a good job."

"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."

"It offers a node-based data integration and processing system connected through a user-friendly drag-and-drop interface. This makes it an excellent choice for data analytics and engineering tasks."

"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."

"I am impressed by the modularity and reusability in KNIME, especially the ability to make small adjustments to object configurations."

"It is a stable solution...It is a scalable solution."

"There are a lot of connectors available in KNIME."

More KNIME Business Hub pros

Cons

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

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

"There is room for improvement in terms of allowing for more code-based features."

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"The license is very expensive."

"The ability to have charts right from the explorer would be an improvement."

"The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests."

"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."

More Dataiku cons

"Expensive to deploy solutions. You need to buy an extra deployment unit."

"The integration with sources and visualisation needs some improvement. The scalability needs improvement."

"The forecasting could be a bit easier."

"It would be good if IBM added help resources to the interface."

"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."

"It's not as user friendly as it could be."

"We have run into a few problems doing some entity matching/analytics."

"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."

More IBM SPSS Modeler cons

"From the point of view of the interface, they can do a little bit better."

"KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too."

"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."

"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."

"I wish there were more video training resources for KNIME. The current videos are very short, and most learning is text-based. Longer training sessions would be helpful, especially for complex flowchart use cases. Webinars focusing on starting projects and analyzing data would also be beneficial."

"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."

"KNIME's documentation is not strong."

"It could be easier to use."

More KNIME Business Hub cons

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 am using the free version of IBM SPSS Modeler, it is the educational edition version."

"Having in mind all four tools from Garner’s top quadrant, the pricing of this tool is competitive and it reflects the quality that it offers."

"If you are in a university and the license is free then you can use the tool without any charges, which is good."

"$5,000 annually."

"Its price is okay for a company, but for personal use, it is considered somewhat expensive."

"It is an expensive product."

"The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly budget. They used to encourage people to use the modeler for development. If ten users use the server with ten licenses, it runs faster. But if forty users use the same appliance, everything slows down. People then think it's not easy to do things and prefer using remote tools like Python to extract data from the database. It's not about being expensive or cheap, but about people's knowledge and experience in how to do the work."

"When you are close to end of quarter, IBM and its partners can get you 60% to 70% discounts, so literally wait for the last day of the quarter for the best prices. You may feel like you are getting robbed if you can't receive a good discount."

More IBM SPSS Modeler pricing and cost advice

"It is free of cost. It is GNU licensed."

"For beginners, the free desktop version is very attractive, but the full server version can be more expensive. I have only used the free version and it offers a fair pricing system. I have been promoting it to others without any compensation or request from the company, simply because I am enthusiastic about it. I am not aware of the pricing for the server version, but it seems to be widely used."

"KNIME is an open-source tool, so it's free to use."

"This is an open-source solution that is free to use."

"With KNIME, you can use the desktop version free of charge as much as you like. I've yet to hit its limits. If I did, I'd have to go to the server version, and for that you have to pay. Fortunately, I don't have to at the moment."

"KNIME is a cost-effective solution because it’s free of cost."

"It's an open-source solution."

"I use the open-source version."

More KNIME Business Hub pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

10%

Computer Software Company

Energy/Utilities Company

Financial Services Firm

12%

Educational Organization

11%

Government

10%

University

Financial Services Firm

11%

Manufacturing Company

10%

University

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	1
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	4
Large Enterprise	31

By reviewers
Company Size	Count
Small Business	20
Midsize Enterprise	16
Large Enterprise	29

Questions from the Community

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

I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is...

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What needs improvement with Dataiku Data Science Studio?

There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow mo...

See all answers

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

My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several compa...

See all answers

What do you like most about IBM SPSS Modeler?

Compared to other tools, the product works much easier to analyze data without coding.

See all answers

What is your experience regarding pricing and costs for IBM SPSS Modeler?

The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly bud...

See all answers

What needs improvement with IBM SPSS Modeler?

The customer comes to you and says they want to deploy it and make a production out of this, which is very difficult ...

See all answers

What do you like most about KNIME?

Since KNIME is a no-code platform, it is easy to work with.

See all answers

What is your experience regarding pricing and costs for KNIME?

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

See all answers

What needs improvement with KNIME?

I have seen the potential to interact with Python, which is currently a bit limited. I am interested in the newer ver...

See all answers

Comparisons

Databricks vs Dataiku

Compared 28% of the time

Alteryx vs Dataiku

Compared 12% of the time

Altair RapidMiner vs Dataiku

Compared 10% of the time

Amazon SageMaker vs Dataiku

Compared 9% of the time

Starburst Galaxy vs Dataiku

Compared 1% of the time

More Dataiku Competitors

Microsoft Power BI vs IBM SPSS Modeler

Compared 24% of the time

IBM SPSS Statistics vs IBM SPSS Modeler

Compared 24% of the time

Altair RapidMiner vs IBM SPSS Modeler

Compared 7% of the time

Alteryx vs IBM SPSS Modeler

Compared 6% of the time

Amazon SageMaker vs IBM SPSS Modeler

Compared 2% of the time

More IBM SPSS Modeler Competitors

Altair RapidMiner vs KNIME Business Hub

Compared 24% of the time

Microsoft Power BI vs KNIME Business Hub

Compared 12% of the time

Microsoft Azure Machine Learning Studio vs KNIME Business Hub

Compared 8% of the time

Alteryx vs KNIME Business Hub

Compared 7% of the time

SAS Enterprise Miner vs KNIME Business Hub

Compared 2% of the time

More KNIME Business Hub Competitors

Product Reports

Buyer's Guide

Dataiku

August 2025

Download Dataiku product report

Buyer's Guide

IBM SPSS Modeler

August 2025

Download IBM SPSS Modeler product report

Buyer's Guide

KNIME Business Hub

August 2025

Download KNIME Business Hub product report

Also Known As

Dataiku DSS

SPSS Modeler

KNIME Analytics Platform

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

IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.

Buy
https://www.ibm.com/products/spss-modeler/pricing

Sign up for the trial
https://www.ibm.com/account/reg/us-en/signup?formid=urx-19947

IBM

KNIME Business Hub offers a no-code interface for data preparation and integration, making analytics and machine learning accessible. Its extensive node library allows seamless workflow execution across various data tasks.

KNIME Business Hub stands out for its user-friendly, no-code platform, promoting efficient data preparation and integration, even with Python and R. Its node library covers extensive data processes from ETL to machine learning. Community support aids users, enhancing productivity with minimal coding. However, its visualization, documentation, and interface require refinement. Larger data tasks face performance hurdles, demanding enhanced cloud connectivity and library expansions for deep learning efficiencies.

What are the most important features of KNIME Business Hub?

No-Code Interface: Simplifies data preparation without coding expertise.
Node Library: Supports diverse data tasks, including ETL and machine learning.
Python and R Integration: Seamlessly connects with popular programming languages.
Automation Capabilities: Enhances workflow productivity.
Visual Workflow Tools: Offers efficient data handling with visual aids.

What benefits and ROI can users expect from KNIME Business Hub?

Increased Productivity: Streamlined data tasks reduce development time.
Community Support: Access to extensive resources and user assistance.
Cost-Effective: Robust performance even on standard hardware.
Accessible Learning Curve: Simplicity allows easy access for educational and professional use.
Enhanced Prototyping: Ideal for decision model testing.

KNIME Business Hub finds application in data transformation, cleansing, and multi-source integration for analytics and reporting. Companies utilize it for predictive modeling, clustering, classification, machine learning, and automating workflows. Its coding-free approach suits educational and professional settings, assisting industries in data wrangling, ETLs, and prototyping decision models.

Knime

Sample Customers

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

Reisebªro Idealtours GmbH, MedeAnalytics, Afni, Israel Electric Corporation, Nedbank Ltd., DigitalGlobe, Vodafone Hungary, Aegon Hungary, Bureau Veritas, Brammer Group, Florida Department of Juvenile Justice, InSites Consulting, Fortis Turkey

Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG

Buyer's Guide

Data Science Platforms

August 2025

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