Cloudera Data Science Workbench vs Darwin comparison

Cloudera and SparkCognition are both solutions in the Data Science Platforms category. Cloudera is ranked #23 with an average rating of 6.0, while SparkCognition is ranked #27. Cloudera holds a 1.3% mindshare in Data Science Platforms, compared to SparkCognition’s 0.3% mindshare. Additionally, 66% of Cloudera users are willing to recommend the solution, compared to 100% of SparkCognition users who would recommend it.

Cloudera Data Science Workb...

Read 2 Cloudera Data Science Workbench reviews

1,008 Views
849 Comparison Views

66% willing to recommend

Darwin

Read 8 Darwin reviews

324 Views
155 Comparison Views

100% willing to recommend

Cloudera Data Science Workb...

Darwin

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

Cloudera Data Science Workbench and Darwin are competing in the domain of data-driven solutions for businesses. Cloudera excels in customer support and pricing, while Darwin leads with its superior features.

Features: Cloudera Data Science Workbench is recognized for robust security, comprehensive enterprise integration, and flexible data management. Darwin is noted for strong machine learning automation, exceptional modeling capabilities, and efficient workflow management.

Ease of Deployment and Customer Service: Cloudera features straightforward deployment and extensive support aimed at enterprise ecosystems, with renowned customer service for availability. Darwin offers streamlined cloud-based deployment for easy use and rapid implementation, with proactive customer service that may be limited by time zones.

Pricing and ROI: Cloudera Data Science Workbench offers a competitive pricing model appealing to enterprises, ensuring quick ROI through effective integration. Darwin, with a higher initial cost, offers significant ROI with its advanced features and automation, validating the investment for cutting-edge solutions.

To learn more, read our detailed Cloudera Data Science Workbench vs. Darwin Report (Updated: March 2025).

Buyer's Guide

Cloudera Data Science Workbench vs. Darwin

March 2025

Download the complete report

Helped 846,617 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:

Categories and Ranking

Cloudera Data Science Workb...

Ranking in Data Science Platforms

23rd

Average Rating

7.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Darwin

Ranking in Data Science Platforms

27th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Cloudera Data Science Workbench is 1.3%, down from 1.7% compared to the previous year. The mindshare of Darwin is 0.3%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

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.

Read full review

AaronCooke

Founder at Helio Summit

Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.

There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.

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

"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."

"In terms of streamlining a lot of the low-level data science work, it does a few things there."

"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."

"The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."

"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."

"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."

"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."

"The thing that I find most valuable is the ability to clean the data."

"The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate."

Cons

"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."

"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."

"The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition."

"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."

"The analyze function takes a lot of time."

"Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model."

"The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin."

"There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."

"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."

Pricing and Cost Advice

"The product is expensive."

"The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."

"In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."

"As far as I understand, my company is not paying anything to use the product."

"I believe our cost is $1,000 per month."

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

846,617 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

34%

Manufacturing Company

10%

Healthcare Company

Computer Software Company

No data available

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...

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 recommend the best-suited card products. These machine-learning models are deployed in o...

See all answers

Ask a question

Earn 20 points

Comparisons

Amazon SageMaker vs Cloudera Data Science Workbench

Compared 30% of the time

Databricks vs Cloudera Data Science Workbench

Compared 29% of the time

Dataiku vs Cloudera Data Science Workbench

Compared 14% of the time

KNIME vs Cloudera Data Science Workbench

Compared 10% of the time

Google Cloud Datalab vs Cloudera Data Science Workbench

Compared 10% of the time

More Cloudera Data Science Workbench Competitors

IBM Watson Studio vs Darwin

Compared 100% of the time

More Darwin Competitors

Product Reports

Buyer's Guide

Data Science Platforms

March 2025

Download Cloudera Data Science Workbench product report

Buyer's Guide

Darwin

April 2025

Download Darwin product report

Also Known As

CDSW

No data available

Overview

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.

Cloudera

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

SparkCognition

Sample Customers

IQVIA, Rush University Medical Center, Western Union

Hunt Oil, Hitachi High-Tech Solutions

Buyer's Guide

Cloudera Data Science Workbench vs. Darwin

March 2025

Free Report: Cloudera Data Science Workbench vs. Darwin

Find out what your peers are saying about Cloudera Data Science Workbench vs. Darwin and other solutions. Updated: March 2025.

DOWNLOAD NOW

846,617 professionals have used our research since 2012.

See our Cloudera Data Science Workbench vs. Darwin report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.