Cloudera Data Science Workbench vs H2O.ai comparison

Cloudera and H2O.ai are both solutions in the Data Science Platforms category. Cloudera is ranked #21 with an average rating of 6.0, while H2O.ai is ranked #22. Cloudera holds a 1.5% mindshare in Data Science Platforms, compared to H2O.ai’s 1.5% mindshare. Additionally, 66% of Cloudera users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

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Executive Summary

We performed a comparison between Cloudera Data Science Workbench and H2O.ai based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Cloudera Data Science Workbench vs. H2O.ai Report (Updated: October 2024).

Buyer's Guide

Cloudera Data Science Workbench vs. H2O.ai

October 2024

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Helped 814,649 peers since 2012

Categories and Ranking

IBM SPSS Statistics

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 H2O.ai is 1.5%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

AbakarAhmat

Software Engineer at a university with 10,001+ employees

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…

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Ismail Peer

Program Management Lead Advisor at Unionbank Philippines

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…

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Rahul Koduru

Director of Data Engineering at Transamerica

Dec 11, 2018

It is helpful, intuitive, and easy to use. The learning curve is not too steep.

One example, we are able to automate life insurance. We have to underwrite policies. When somebody applies for a policy, we take their blood, then assign them a risk: substandard, standard, preferred, etc. Depending on this, we price our products. Usually the process is that you take the blood, then it goes to a lab and we get the lab results back, then an underwriter takes a look at the lab results. This is usually done in a two week time frame to get a rating. We were able to build models to automate all of this, and now, it happens in real-time. Somebody can apply online and get issued a policy right away.

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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

"In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."

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

"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."

"One feature I found very valuable was the analysis of variance (ANOVA)."

"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."

"You can quickly build models because it does the work for you."

"The most valuable features are the small learning curve and its ability to hold a lot of data."

"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."

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."

"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."

"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."

"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."

"It is helpful, intuitive, and easy to use. The learning curve is not too steep."

"The ease of use in connecting to our cluster machines."

Cons

"I would like SPSS to improve its integration with other data-filing IBM tools. I also think its duration with data, utilization, and graphics could be better."

"Better documentation on how to use macros."

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

"One of the areas that should be similar to Minitabs is the use of blogs. The Minitabs blog helps users understand the tools and gives lots of practical examples. Following the SPSS manual is cumbersome. It's a good, exhaustive manual, but it's not practical to use. With Minitabs, you can go to the blogs and find specific articles written about various components and it's very helpful. Without blogs, we find SPSS more complicated."

"IBM SPSS Statistics could improve the visual outputs where you are producing, for example, a graph for a company board of directors, or an advert."

"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."

"There is a learning curve; it's not very steep, but there is one."

"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."

More IBM SPSS Statistics 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."

"The model management features could be improved."

"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."

"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."

"I would like to see more features related to deployment."

"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."

"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."

Pricing and Cost Advice

"The price of IBM SPSS Statistics could improve."

"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."

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

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"We think that IBM SPSS is expensive for this function."

"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 IBM SPSS Statistics pricing and cost advice

"The product is expensive."

"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."

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814,649 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

16%

University

10%

Computer Software Company

Manufacturing Company

Financial Services Firm

35%

Manufacturing Company

11%

Healthcare Company

Government

Financial Services Firm

19%

Computer Software Company

11%

Manufacturing 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.

See all answers

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...

See all answers

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.

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

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Comparisons

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Compared 14% of the time

IBM SPSS Modeler vs IBM SPSS Statistics

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TIBCO Statistica vs IBM SPSS Statistics

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Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

Weka vs IBM SPSS Statistics

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Amazon SageMaker vs Cloudera Data Science Workbench

Compared 29% of the time

Databricks vs Cloudera Data Science Workbench

Compared 28% of the time

Dataiku vs Cloudera Data Science Workbench

Compared 13% of the time

Google Cloud Datalab vs Cloudera Data Science Workbench

Compared 10% of the time

Microsoft Azure Machine Learning Studio vs Cloudera Data Science Workbench

Compared 9% of the time

More Cloudera Data Science Workbench Competitors

Databricks vs H2O.ai

Compared 20% of the time

Dataiku vs H2O.ai

Compared 19% of the time

Amazon SageMaker vs H2O.ai

Compared 16% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 15% of the time

KNIME vs H2O.ai

Compared 11% of the time

More H2O.ai Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

October 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Data Science Platforms

October 2024

Download Cloudera Data Science Workbench product report

Buyer's Guide

Data Science Platforms

October 2024

Download H2O.ai product report

Also Known As

SPSS Statistics

CDSW

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IBM

Cloudera

H2O.ai

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.

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

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

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco

Buyer's Guide

Cloudera Data Science Workbench vs. H2O.ai

October 2024

Free Report: Cloudera Data Science Workbench vs. H2O.ai

Find out what your peers are saying about Cloudera Data Science Workbench vs. H2O.ai and other solutions. Updated: October 2024.

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