Dataiku vs H2O.ai comparison

Comparison Buyer's Guide

Executive Summary

We performed a comparison between Dataiku 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 Dataiku vs. H2O.ai Report (Updated: July 2024).

Buyer's Guide

Dataiku vs. H2O.ai

July 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of July 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 3.0%, up from 2.3% compared to the previous year. The mindshare of Dataiku is 12.9%, up from 6.8% compared to the previous year. The mindshare of H2O.ai is 1.9%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Unique Categories:

Data Mining

21.6%

No other categories found

Featured Reviews

Ali Bin Tahir

Research Executive at NielsenIQ

Mar 1, 2024

Provides comprehensive data analysis and has a simple setup process

We use the product to conduct multiple and diverse statistical analyses across various datasets The software offers consistency across multiple research projects helping us with predictive analytics capabilities. The product’s most valuable capability is to handle large datasets and ensure…

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RangeshKunnavakkam

Consultant at Netsoft

May 17, 2024

Gives different aspects of modeling approaches and good for multiple teams' collaboration

I used DataRobot. Dataiku has a different kind of structure to it. It's not financially heavy like DataRobot, which caters more to financial companies, like banks. Dataiku doesn't have that yet. I think they are also working on that area. But yeah, there are some key differences between the two products. DataRobot has an additional feature with financial firms that it creates all these financial metrics when you run a time series analysis. Those things I have not seen in Dataiku. If any financial company is choosing between DataRobot and Dataiku, they will definitely go for DataRobot because it creates all these financial metrics. It creates deltas, time series, time difference fields, and things like that. So, that is an added feature that DataRobot has.

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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

"Most of the product features are good but I particularly like the linear regression analysis."

"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."

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

"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."

"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."

"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."

"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."

"The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS."

More IBM SPSS Statistics pros

"The solution is quite stable."

"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."

"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."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"The most valuable feature is the set of visual data preparation tools."

"Data Science Studio's data science model is very useful."

"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."

"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."

"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."

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

"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."

Cons

"The solution needs more planning tools and capabilities."

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

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

"I feel that when it comes to conducting multiple analyses, there could be more detailed information provided. Currently, the software gives a summary and an overview, but it would be beneficial to have specific details for each product or variable."

"This solution is not suitable for use with Big Data."

"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."

"The product should provide more ways to import data and export results that are user-friendly for high-level executives."

"Better documentation on how to use macros."

More IBM SPSS Statistics cons

"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."

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."

"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."

"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."

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

More Dataiku cons

"The model management features could be improved."

"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."

"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."

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

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

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

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."

"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."

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

"The price of IBM SPSS Statistics could improve."

"The price of this solution is a little bit high, which was a problem for my company."

"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."

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

More IBM SPSS Statistics pricing and cost advice

"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."

"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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793,295 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

University

17%

Educational Organization

13%

Computer Software Company

Financial Services Firm

18%

Educational Organization

15%

Manufacturing Company

Computer Software Company

Financial Services Firm

20%

Computer Software Company

11%

Manufacturing Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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 is your experience regarding pricing and costs for Dataiku Data Science Studio?

Pricing is pretty steep. Dataiku is also not that cheap. It depends on the client and how much they want to spend tow...

See all answers

What needs improvement with Dataiku Data Science Studio?

The no-code/low-code aspect, where DataRobot doesn't need much coding at all. Dataiku still needs some coding, and th...

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What is your primary use case for Dataiku Data Science Studio?

My current client has Dataiku. We do sentiment analysis and some small large language models right now. We use Dataik...

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Comparisons

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Weka vs IBM SPSS Statistics

Compared 10% of the time

Anaconda vs IBM SPSS Statistics

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Databricks vs Dataiku

Compared 39% of the time

KNIME vs Dataiku

Compared 13% of the time

Alteryx vs Dataiku

Compared 11% of the time

RapidMiner vs Dataiku

Compared 9% of the time

Starburst Galaxy vs Dataiku

Compared 1% of the time

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Databricks vs H2O.ai

Compared 21% of the time

Amazon SageMaker vs H2O.ai

Compared 17% of the time

KNIME vs H2O.ai

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 13% of the time

IBM Watson Studio vs H2O.ai

Compared 7% of the time

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Product Reports

Buyer's Guide

IBM SPSS Statistics

July 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Dataiku

July 2024

Download Dataiku product report

Buyer's Guide

Data Science Platforms

June 2024

Download H2O.ai product report

Also Known As

SPSS Statistics

Dataiku DSS

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IBM

Dataiku

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.”

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

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

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

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

Buyer's Guide

Dataiku vs. H2O.ai

July 2024

Free Report: Dataiku vs. H2O.ai

Find out what your peers are saying about Dataiku vs. H2O.ai and other solutions. Updated: July 2024.

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