Cloudera Data Science Workbench vs Domino Data Science Platform comparison

Cloudera and Domino Data Lab are both solutions in the Data Science Platforms category. Cloudera is ranked #22 with an average rating of 6.0, while Domino Data Lab is ranked #15 with an average rating of 8.0. Cloudera holds a 1.5% mindshare in Data Science Platforms, compared to Domino Data Lab’s 2.6% mindshare. Additionally, 66% of Cloudera users are willing to recommend the solution, compared to 100% of Domino Data Lab users who would recommend it.

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Executive SummaryUpdated on Dec 5, 2024

Domino Data Science Platform and Cloudera Data Science Workbench are both in the data science platform category. Domino appears to have the advantage in collaboration and scalability, while Cloudera is stronger in integration within its data ecosystem.

Features: Domino focuses on collaborative data science workflows with robust project management, computational environment customization, and enhanced scalability. Cloudera integrates seamlessly with existing Cloudera services and provides strong support for machine learning models and automated pipeline management.

Ease of Deployment and Customer Service: Domino's cloud-optimized deployment model offers flexibility in resource management and comprehensive onboarding support. Cloudera performs well in private cloud or on-premises setups, benefiting from its integration with Cloudera's data platform, complemented by extensive documentation and support resources.

Pricing and ROI: Domino, while having a higher initial setup cost, is noted for delivering a strong ROI through enhanced productivity and collaboration. Cloudera offers competitive pricing, especially for existing customers who harness integrated solutions and support, leading to quicker value realization from their investment.

To learn more, read our detailed Cloudera Data Science Workbench vs. Domino Data Science Platform Report (Updated: November 2024).

Buyer's Guide

Cloudera Data Science Workbench vs. Domino Data Science Platform

November 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 Domino Data Science Platform is 2.6%, down from 2.7% 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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AHMEDSAHRAOUI

Machine Learning Engineer at Unemployed

Accelerated machine learning model development with seamless deployment

We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they…

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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 are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial."

"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."

"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."

"SPSS is quite robust and quicker in terms of providing you the output."

"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."

"It has the ability to easily change any variable in our research."

"in terms of the simplicity, I think the SPSS basic can handle it."

"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."

More IBM SPSS Statistics pros

"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 scalability of the solution is good; I'd rate it four out of five."

"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."

Cons

"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people. I found that everything was very confusing at first. Having clear documentation would help alleviate that."

"IBM SPSS Statistics does not keep you close to your data like KNIME."

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

"Technical support needs some improvement, as they do not respond as quickly as we would like."

"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."

"The solution needs to improve forecasting using time series analysis."

"It could provide even more in the way of automation as there are many opportunities."

"The solution needs more planning tools and capabilities."

More IBM SPSS Statistics 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."

"The predictive analysis feature needs improvement."

"The deployment of large language models (LLMs) could be improved."

Pricing and Cost Advice

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

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

"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."

"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."

"The price of IBM SPSS Statistics could improve."

"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."

More IBM SPSS Statistics pricing and cost advice

"The product is expensive."

Information not available

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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

32%

Manufacturing Company

11%

Computer Software Company

Insurance 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.

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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...

See all answers

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 needs improvement with Domino Data Science Platform?

The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that can...

See all answers

What is your primary use case for Domino Data Science Platform?

We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-e...

See all answers

What advice do you have for others considering Domino Data Science Platform?

It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying so...

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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

Weka vs IBM SPSS Statistics

Compared 10% 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

Dataiku vs Cloudera Data Science Workbench

Compared 14% 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

More Cloudera Data Science Workbench Competitors

Amazon SageMaker vs Domino Data Science Platform

Compared 36% of the time

Databricks vs Domino Data Science Platform

Compared 28% of the time

Dataiku vs Domino Data Science Platform

Compared 9% of the time

Microsoft Azure Machine Learning Studio vs Domino Data Science Platform

Compared 7% of the time

Alteryx vs Domino Data Science Platform

Compared 6% of the time

More Domino Data Science Platform 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

Data Science Platforms

December 2024

Download Domino Data Science Platform product report

Also Known As

SPSS Statistics

CDSW

Domino Data Lab Platform

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Cloudera

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Domino Data Lab

Interactive Demo

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IBM

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Cloudera

Domino Data Lab

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.

Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.

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

Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life

Buyer's Guide

Cloudera Data Science Workbench vs. Domino Data Science Platform

November 2024

Free Report: Cloudera Data Science Workbench vs. Domino Data Science Platform

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