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Cloudera Data Science Workbench vs Dataiku comparison

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Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM SPSS Statistics
Sponsored
Ranking in Data Science Platforms
10th
Average Rating
8.0
Number of Reviews
37
Ranking in other categories
Data Mining (3rd)
Cloudera Data Science Workb...
Ranking in Data Science Platforms
21st
Average Rating
7.0
Number of Reviews
2
Ranking in other categories
No ranking in other categories
Dataiku
Ranking in Data Science Platforms
7th
Average Rating
8.0
Number of Reviews
8
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% 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 11.5%, up from 7.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AbakarAhmat - PeerSpot reviewer
Enhancing survey analysis that provides valued insightfulness
I used traditional tools where I would prepare data, click through menus, and use SQL Server for data visualization. We switched to IBM SPSS because it offers strong certification and aligns well with the standards we prioritize in our surveys. In terms of popularity, it stands out as the top choice in the market, especially in the research and university domains. Many different organizations and institutions use SPSS for statistical analytics. While there are other tools like MCLab and similar options available, SPSS is the most renowned and widely used among them.
Ismail Peer - PeerSpot reviewer
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.
Sabrine Bendimerad - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"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 into multidimensional setup space. It's the multidimensional space facility that is most useful."
"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."
"The most valuable feature is its robust statistical analysis capabilities."
"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."
"Most of the product features are good but I particularly like the linear regression analysis."
"The solution has numerous valuable features. We particularly like custom tabs. It's very useful. We end up analyzing a lot of software data, so features related to custom tabs are really helpful."
"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."
"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."
"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."
"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."
"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 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."
"The most valuable feature is the set of visual data preparation tools."
"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 solution is quite stable."
 

Cons

"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."
"Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."
"Perhaps in terms of visualization. It's not really easy to do some data visualization, just simple, descriptive analysis in SPSS. I think that could be an area for improvement."
"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 product should provide more ways to import data and export results that are user-friendly for high-level executives."
"Needs more statistical modelling functions."
"I feel that when it comes to conducting multiple analyses, there could be more detailed information provided. Currently, the software gives a summary and an overview, but it would be beneficial to have specific details for each product or variable."
"It could provide even more in the way of automation as there are many opportunities."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"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."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"I think it would help if Data Science Studio added some more features and improved the data model."
"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."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"The ability to have charts right from the explorer would be an improvement."
 

Pricing and Cost Advice

"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."
"More affordable training for new staff members."
"We think that IBM SPSS is expensive for this function."
"While the pricing of the product may be higher, the accompanying service and features justify the investment."
"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."
"I rate the tool's pricing a five out of ten."
"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."
"The product is expensive."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
University
9%
Computer Software Company
9%
Manufacturing Company
8%
Financial Services Firm
35%
Manufacturing Company
11%
Healthcare Company
9%
Government
7%
Financial Services Firm
18%
Educational Organization
16%
Manufacturing Company
9%
Computer Software Company
8%
 

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.
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...
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...
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'...
What needs improvement with Cloudera Data Science Workbench?
The tool's MLOps is not good. It's pricing also needs to improve.
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...
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...
What is your primary use case for Dataiku Data Science Studio?
We use the solution for data science and machine learning.
 

Also Known As

SPSS Statistics
CDSW
Dataiku DSS
 

Learn More

Video not available
 

Overview

 

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
Find out what your peers are saying about Cloudera Data Science Workbench vs. Dataiku and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.