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Amazon SageMaker vs Cloudera Data Science Workbench comparison

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Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM SPSS Statistics
Sponsored
Ranking in Data Science Platforms
10th
Average Rating
8.0
Number of Reviews
37
Ranking in other categories
Data Mining (3rd)
Amazon SageMaker
Ranking in Data Science Platforms
5th
Average Rating
7.6
Reviews Sentiment
9.1
Number of Reviews
27
Ranking in other categories
AI Development Platforms (4th)
Cloudera Data Science Workb...
Ranking in Data Science Platforms
21st
Average Rating
7.0
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

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 Amazon SageMaker is 7.7%, down from 10.4% compared to the previous year. The mindshare of Cloudera Data Science Workbench is 1.5%, down from 1.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AbakarAhmat - PeerSpot reviewer
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…
Natu Lauchande - PeerSpot reviewer
Feb 27, 2024
Easy to use and manage, but the documentation does not have a lot of information
We use the product for deploying machine learning models. We use it for the machine learning model development process We're currently implementing a project on a cross-selling model. It is like a standard XGBoost model. I’m evaluating the tool to see whether it will improve the workflow.…
Ismail Peer - PeerSpot reviewer
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…

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 feature is the user interface because you don't need to write code."
"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."
"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."
"in terms of the simplicity, I think the SPSS basic can handle it."
"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."
"The most valuable feature is its robust statistical analysis capabilities."
"You can quickly build models because it does the work for you."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The most valuable feature of Amazon SageMaker is SageMaker Studio."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"We were able to use the product to automate processes."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"They are doing a good job of evolving."
"It's user-friendly for business teams as they can understand many aspects through the AWS interface."
"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."
 

Cons

"I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input."
"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."
"The reports could be better."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"Better documentation on how to use macros."
"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."
"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
"The documentation must be made clearer and more user-friendly."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."
"There are other better solutions for large data, such as Databricks."
"AI is a new area and AWS needs to have an internship training program available."
"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."
"The solution is complex to use."
"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."
"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."
 

Pricing and Cost Advice

"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."
"We think that IBM SPSS is expensive for this function."
"It's quite expensive, but they do a special deal for universities."
"While the pricing of the product may be higher, the accompanying service and features justify the investment."
"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."
"More affordable training for new staff members."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"The pricing could be better, especially for querying. The per-query model feels expensive."
"The solution is relatively cheaper."
"I would rate the solution's price a ten out of ten since it is very high."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"There is no license required for the solution since you can use it on demand."
"The tool's pricing is reasonable."
"SageMaker is worth the money for our use case."
"The product is expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
University
10%
Computer Software Company
9%
Manufacturing Company
8%
Financial Services Firm
18%
Educational Organization
14%
Computer Software Company
11%
Manufacturing Company
8%
Financial Services Firm
35%
Manufacturing Company
11%
Healthcare Company
9%
Government
7%
 

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.
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...
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.
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designe...
What do you like most about Amazon SageMaker?
We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to Cha...
What is your experience regarding pricing and costs for Amazon SageMaker?
The license cost for Amazon SageMaker ranges between seven thousand to fifteen thousand dollars per month depending o...
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'...
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 recommen...
 

Also Known As

SPSS Statistics
AWS SageMaker, SageMaker
CDSW
 

Learn More

 

Overview

 

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
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
IQVIA, Rush University Medical Center, Western Union
Find out what your peers are saying about Amazon SageMaker vs. Cloudera Data Science Workbench and other solutions. Updated: October 2024.
814,649 professionals have used our research since 2012.