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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
Sep 21, 2023
Enhancing survey analysis that provides valued insightfulness
I use it to analyze questionnaire surveys related to a product, solution, or application, such as open data services, which I provide to consumers and end-users. These surveys contain evaluation assessments, and I use SPSS to analyze the responses The most valuable feature is its robust…
Ismail Peer - PeerSpot reviewer
Feb 13, 2024
Useful for data science modeling but improvement is needed in MLOps and pricing
We have different use cases. Our banking use case uses machine learning to identify customer life events and recommend the best-suited card products. These machine-learning models are deployed in our environment, where they run on a scheduled basis. We rely on the platform for every data science…
Sabrine Bendimerad - PeerSpot reviewer
Jun 11, 2024
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 features mainly include factor analysis, correlation analysis, and geographic analysis."
"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."
"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."
"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."
"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."
"It has the ability to easily change any variable in our research."
"The most valuable features are the small learning curve and its ability to hold a lot of data."
"You can quickly build models because it does the work for you."
"The Cloudera Data Science Workbench is customizable and easy to use."
"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 most valuable feature is the set of visual data preparation tools."
"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 like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"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."
"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."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
 

Cons

"The solution needs more planning tools and capabilities."
"The design of the experience can be improved."
"It could provide even more in the way of automation as there are many opportunities."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"The solution needs to improve forecasting using time series analysis."
"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."
"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior. You can call it augmented analysis or predictive analysis. The bottom line is it should have more graphical and analytical capabilities."
"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."
"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."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"I think it would help if Data Science Studio added some more features and improved the data model."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"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."
"The ability to have charts right from the explorer would be an improvement."
 

Pricing and Cost Advice

"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."
"More affordable training for new staff members."
"While the pricing of the product may be higher, the accompanying service and features justify the investment."
"I rate the tool's pricing a five out of ten."
"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."
"The price of this solution is a little bit high, which was a problem for my company."
"The price of IBM SPSS Statistics could improve."
"It's quite expensive, but they do a special deal for universities."
"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
16%
University
10%
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?
While the pricing of the product may be higher, the accompanying service and features justify the investment. However...
What needs improvement with IBM SPSS Statistics?
In some cases, the product takes time to load a large dataset. They could improve this particular area.
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.
814,649 professionals have used our research since 2012.