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Cloudera Data Science Workbench vs Dataiku comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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

Cloudera Data Science Workb...
Ranking in Data Science Platforms
22nd
Average Rating
7.0
Reviews Sentiment
6.9
Number of Reviews
2
Ranking in other categories
No ranking in other categories
Dataiku
Ranking in Data Science Platforms
7th
Average Rating
8.0
Reviews Sentiment
7.2
Number of Reviews
9
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Cloudera Data Science Workbench is 1.5%, down from 1.7% compared to the previous year. The mindshare of Dataiku is 12.1%, up from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

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

"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 rate the overall product as eight out of ten."
"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."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"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."
"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."
"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 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."
 

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."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."
"I think it would help if Data Science Studio added some more features and improved the data model."
"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."
"The ability to have charts right from the explorer would be an improvement."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"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."
"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."
 

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
36%
Manufacturing Company
11%
Healthcare Company
9%
Computer Software Company
6%
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 Cloudera Data Science Workbench?
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...
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 recommend the best-suited card products. These machine-learning models are deployed in o...
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 integrating GitHub with Dataiku was complicated. While it was theoretically possible to us...
What is your primary use case for Dataiku Data Science Studio?
We use the solution for data science and machine learning.
 

Also Known As

CDSW
Dataiku DSS
 

Learn More

 

Overview

 

Sample Customers

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: January 2025.
831,265 professionals have used our research since 2012.