Cloudera Data Science Workbench vs Dataiku comparison

Cloudera and Dataiku are both solutions in the Data Science Platforms category. Cloudera is ranked #22 with an average rating of 6.0, while Dataiku is ranked #7 with an average rating of 7.5. Cloudera holds a 1.5% mindshare in Data Science Platforms, compared to Dataiku’s 11.8% mindshare. Additionally, 66% of Cloudera users are willing to recommend the solution, compared to 100% of Dataiku users who would recommend it.

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Executive SummaryUpdated on Dec 5, 2024

Dataiku and Cloudera Data Science Workbench compete in the data science platform market. Dataiku has an edge in user satisfaction due to its ease of use and integration capabilities, while Cloudera Data Science Workbench is noted for its extensive functionality, although it may have a steeper learning curve.

Features: Dataiku thrives with its intuitive drag-and-drop interface, automated machine learning, and seamless integration with multiple data sources, making it suitable for both beginners and advanced users. Cloudera Data Science Workbench offers robust support for coding in R, Python, and Scala, enhanced by built-in security features designed for large enterprises, providing flexibility for data scientists who prefer programming.

Ease of Deployment and Customer Service: Dataiku's deployment process is regarded as quicker and more straightforward, attributed to a streamlined installation process and accessible customer support, beneficial for businesses seeking rapid time-to-value. Cloudera Data Science Workbench requires more technical expertise for deployment, supported by strong customer support, catering predominantly to larger organizations desiring enterprise-grade solutions.

Pricing and ROI: Dataiku often presents a more attractive setup cost, offering scalability at an affordable entry price, with users noting high ROI due to its ease of integration and fast setup. Cloudera Data Science Workbench, while potentially involving higher upfront costs, is justified by its comprehensive feature set, indicating a higher long-term ROI for enterprises in need of its advanced capabilities.

To learn more, read our detailed Cloudera Data Science Workbench vs. Dataiku Report (Updated: December 2024).

Buyer's Guide

Cloudera Data Science Workbench vs. Dataiku

December 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of December 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.7%, up from 2.7% compared to the previous year. The mindshare of Cloudera Data Science Workbench is 1.5%, down from 1.8% compared to the previous year. The mindshare of Dataiku is 11.8%, up from 7.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Md Masudul Hassan

Agricultural Finance and IT Researcher at Sher-e-Bangla Agricultural University

Comprehensive data analysis capabilities with a user-friendly interface, providing an efficient and reliable platform for researchers and analysts

I believe that offering short-term SPSS licenses, perhaps when customer sourcing is available, could make it more affordable. These licenses shouldn't include features tailored for universities or large sales organizations. Instead, they could offer discounts or additional facilities for smaller entities to access the software. In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options. For example, offering basic features to the first hundred users can help them become familiar with the software and its capabilities. This approach encourages users to upgrade to higher tiers as they become more experienced and require additional functionality.

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Ismail Peer

Program Management Lead Advisor at Unionbank Philippines

Useful for data science modeling but improvement is needed in MLOps and pricing

If you don't configure CDSW well, then it might be not useful for you. Deploying the tool can vary in complexity, but most of the time, it's relatively simple and straightforward. Triggering a job from data to production is easy, as the platform automates the deployment process. However, ensuring optimal resource allocation is essential for smooth operations.

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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

Quotes from Members

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

Pros

"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."

"One feature I found very valuable was the analysis of variance (ANOVA)."

"The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it."

"In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."

"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."

"It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation."

"The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS."

"Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things."

More IBM SPSS Statistics pros

"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."

"The Cloudera Data Science Workbench is customizable and easy to use."

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

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

"The solution is quite stable."

"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 is the set of visual data preparation tools."

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

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

More Dataiku pros

Cons

"It could allow adding color to data models to make them easier to interpret."

"Better documentation on how to use macros."

"Improvements are needed in the user interface, particularly in terms of user-friendliness."

"The reports could be better."

"I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input."

"The solution needs more planning tools and capabilities."

"I would like SPSS to improve its integration with other data-filing IBM tools. I also think its duration with data, utilization, and graphics could be better."

"This solution is not suitable for use with Big Data."

More IBM SPSS Statistics cons

"The tool's MLOps is not good. It's pricing also needs to improve."

"Running this solution requires a minimum of 12GB to 16GB of RAM."

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

"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."

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

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

"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."

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

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

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

More Dataiku cons

Pricing and Cost Advice

"More affordable training for new staff members."

"It's quite expensive, but they do a special deal for universities."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"The price of this solution is a little bit high, which was a problem for my company."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"We think that IBM SPSS is expensive for this function."

"I rate the tool's pricing a five out of ten."

"The price of IBM SPSS Statistics could improve."

More IBM SPSS Statistics pricing and cost advice

"The product is expensive."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

Financial Services Firm

36%

Manufacturing Company

12%

Healthcare Company

Computer Software Company

Financial Services Firm

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

See all answers

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

The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...

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What needs improvement with IBM SPSS Statistics?

IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...

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What do you like most about Cloudera Data Science Workbench?

I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don'...

See all answers

What needs improvement with Cloudera Data Science Workbench?

The tool's MLOps is not good. It's pricing also needs to improve.

See all answers

What is your primary use case for Cloudera Data Science Workbench?

We have different use cases. Our banking use case uses machine learning to identify customer life events and recommen...

See all answers

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?

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integratin...

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

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 16% of the time

Alteryx vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 11% of the time

KNIME vs IBM SPSS Statistics

Compared 2% of the time

More IBM SPSS Statistics Competitors

Databricks vs Cloudera Data Science Workbench

Compared 31% of the time

Amazon SageMaker vs Cloudera Data Science Workbench

Compared 30% of the time

Microsoft Azure Machine Learning Studio vs Cloudera Data Science Workbench

Compared 9% of the time

Google Cloud Datalab vs Cloudera Data Science Workbench

Compared 9% of the time

KNIME vs Cloudera Data Science Workbench

Compared 7% of the time

More Cloudera Data Science Workbench Competitors

Databricks vs Dataiku

Compared 37% of the time

KNIME vs Dataiku

Compared 15% of the time

Alteryx vs Dataiku

Compared 11% of the time

RapidMiner vs Dataiku

Compared 11% of the time

IBM SPSS Modeler vs Dataiku

Compared 1% of the time

More Dataiku Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

December 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Data Science Platforms

December 2024

Download Cloudera Data Science Workbench product report

Buyer's Guide

Dataiku

December 2024

Download Dataiku product report

Also Known As

SPSS Statistics

CDSW

Dataiku DSS

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IBM

Cloudera

Dataiku

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis.

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.

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

IQVIA, Rush University Medical Center, Western Union

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

Buyer's Guide

Cloudera Data Science Workbench vs. Dataiku

December 2024

Free Report: Cloudera Data Science Workbench vs. Dataiku

Find out what your peers are saying about Cloudera Data Science Workbench vs. Dataiku and other solutions. Updated: December 2024.

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