Cloudera Data Science Workbench vs. IBM SPSS Modeler

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Data Science Workbench
Score 6.7 out of 10
N/A
Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.N/A
IBM SPSS Modeler
Score 7.8 out of 10
N/A
IBM SPSS Modeler is a visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations can use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets.
$499
per month
Pricing
Cloudera Data Science WorkbenchIBM SPSS Modeler
Editions & Modules
No answers on this topic
IBM SPSS Modeler Personal
4,670
per year
IBM SPSS Modeler Professional
7,000
per year
IBM SPSS Modeler Premium
11,600
per year
IBM SPSS Modeler Gold
contact IBM
per year
Offerings
Pricing Offerings
Data Science WorkbenchIBM SPSS Modeler
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeOptional
Additional DetailsIBM SPSS Modeler Personal enables users to design and build predictive models right from the desktop. IBM SPSS Modeler Professional extends SPSS Modeler Personal with enterprise-scale in-database mining, SQL pushback, collaboration and deployment, champion/challenger, A/B testing, and more. IBM SPSS Modeler Premium extends SPSS Modeler Professional by including unstructured data analysis with integrated, natural language text and entity and social network analytics. IBM SPSS Modeler Gold extends SPSS Modeler Premium with the ability to build and deploy predictive models directly into the business process to aid in decision making. This is achieved with Decision Management which combines predictive analytics with rules, scoring, and optimization to deliver recommended actions at the point of impact.
More Pricing Information
Community Pulse
Cloudera Data Science WorkbenchIBM SPSS Modeler
Top Pros
Top Cons
Features
Cloudera Data Science WorkbenchIBM SPSS Modeler
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Cloudera Data Science Workbench
7.5
2 Ratings
12% below category average
IBM SPSS Modeler
-
Ratings
Connect to Multiple Data Sources7.02 Ratings00 Ratings
Extend Existing Data Sources8.02 Ratings00 Ratings
Automatic Data Format Detection7.02 Ratings00 Ratings
MDM Integration8.02 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Cloudera Data Science Workbench
7.6
2 Ratings
10% below category average
IBM SPSS Modeler
-
Ratings
Visualization7.12 Ratings00 Ratings
Interactive Data Analysis8.02 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Cloudera Data Science Workbench
7.8
2 Ratings
6% below category average
IBM SPSS Modeler
-
Ratings
Interactive Data Cleaning and Enrichment7.02 Ratings00 Ratings
Data Transformations8.02 Ratings00 Ratings
Data Encryption8.02 Ratings00 Ratings
Built-in Processors8.02 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Cloudera Data Science Workbench
7.6
2 Ratings
11% below category average
IBM SPSS Modeler
-
Ratings
Multiple Model Development Languages and Tools8.02 Ratings00 Ratings
Automated Machine Learning7.01 Ratings00 Ratings
Single platform for multiple model development7.12 Ratings00 Ratings
Self-Service Model Delivery8.12 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Cloudera Data Science Workbench
8.0
2 Ratings
7% below category average
IBM SPSS Modeler
-
Ratings
Flexible Model Publishing Options8.12 Ratings00 Ratings
Security, Governance, and Cost Controls7.82 Ratings00 Ratings
Best Alternatives
Cloudera Data Science WorkbenchIBM SPSS Modeler
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
Medium-sized Companies
Mathematica
Mathematica
Score 8.2 out of 10
Mathematica
Mathematica
Score 8.2 out of 10
Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Dataiku
Dataiku
Score 7.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Cloudera Data Science WorkbenchIBM SPSS Modeler
Likelihood to Recommend
9.0
(3 ratings)
10.0
(6 ratings)
Support Rating
7.9
(2 ratings)
10.0
(1 ratings)
User Testimonials
Cloudera Data Science WorkbenchIBM SPSS Modeler
Likelihood to Recommend
Cloudera
Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.
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IBM
Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.
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Pros
Cloudera
  • One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
  • For larger organizations/teams, it lets you be self reliant
  • As it sits on your cluster, it has very easy access of all the data on the HDFS
  • Linking with Github is a very good way to keep the code versions intact
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IBM
  • Combine text and data
  • Provide facilities for all phases of the data mining process.
  • Use a node and stream paradigm to easily and quickly create models.
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Cons
Cloudera
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
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IBM
  • Has very old style graphs, with lots of limitations.
  • Some advanced statistical functions cannot be done through the menu.
  • The data connectivity is not that extensive.
  • It's an expensive tool.
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Support Rating
Cloudera
Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.
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IBM
The online support board is helpful and the free add ons are incredibly appreciated.
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Alternatives Considered
Cloudera
Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
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IBM
When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.
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Return on Investment
Cloudera
  • Paid off for demonstration purposes.
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IBM
  • Positive - Ease of decision making and reduction in product life cycle time.
  • Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
  • Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.
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ScreenShots

IBM SPSS Modeler Screenshots

Screenshot of Use a single run to test multiple modeling methods, compare results and select which model to deploy. Quickly choose the best performing algorithm based on model performance.Screenshot of Explore geographic data, such as latitude and longitude, postal codes and addresses. Combine it with current and historical data for better insights and predictive accuracy.Screenshot of Capture key concepts, themes, sentiments and trends by analyzing unstructured text data. Uncover insights in web activity, blog content, customer feedback, emails and social media comments.Screenshot of Use R, Python, Spark, Hadoop and other open source technologies to amplify the power of your analytics. Extend and complement these technologies for more advanced analytics while you keep control.