Cloudera Data Science Workbench vs. NVIDIA RAPIDS

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
NVIDIA RAPIDS
Score 9.3 out of 10
N/A
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs. Users can run data science workflows with high-speed GPU compute and parallelize data loading, data manipulation, and machine learning for 50X faster end-to-end data science pipelines.N/A
Pricing
Cloudera Data Science WorkbenchNVIDIA RAPIDS
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Data Science WorkbenchNVIDIA RAPIDS
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Cloudera Data Science WorkbenchNVIDIA RAPIDS
Top Pros
Top Cons
Features
Cloudera Data Science WorkbenchNVIDIA RAPIDS
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
NVIDIA RAPIDS
9.1
2 Ratings
7% above category average
Connect to Multiple Data Sources7.02 Ratings9.62 Ratings
Extend Existing Data Sources8.02 Ratings8.82 Ratings
Automatic Data Format Detection7.02 Ratings9.02 Ratings
MDM Integration8.02 Ratings9.01 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
NVIDIA RAPIDS
9.4
2 Ratings
11% above category average
Visualization7.12 Ratings9.42 Ratings
Interactive Data Analysis8.02 Ratings9.42 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Cloudera Data Science Workbench
7.8
2 Ratings
5% below category average
NVIDIA RAPIDS
8.9
2 Ratings
8% above category average
Interactive Data Cleaning and Enrichment7.02 Ratings7.82 Ratings
Data Transformations8.02 Ratings9.42 Ratings
Data Encryption8.02 Ratings9.01 Ratings
Built-in Processors8.02 Ratings9.42 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
NVIDIA RAPIDS
9.2
2 Ratings
8% above category average
Multiple Model Development Languages and Tools8.02 Ratings9.01 Ratings
Automated Machine Learning7.01 Ratings9.42 Ratings
Single platform for multiple model development7.12 Ratings9.42 Ratings
Self-Service Model Delivery8.12 Ratings9.01 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
NVIDIA RAPIDS
9.2
2 Ratings
7% above category average
Flexible Model Publishing Options8.12 Ratings9.42 Ratings
Security, Governance, and Cost Controls7.82 Ratings9.01 Ratings
Best Alternatives
Cloudera Data Science WorkbenchNVIDIA RAPIDS
Small Businesses
Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
Medium-sized Companies
Mathematica
Mathematica
Score 8.3 out of 10
Mathematica
Mathematica
Score 8.3 out of 10
Enterprises
Alteryx
Alteryx
Score 9.0 out of 10
Alteryx
Alteryx
Score 9.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Cloudera Data Science WorkbenchNVIDIA RAPIDS
Likelihood to Recommend
9.0
(3 ratings)
10.0
(2 ratings)
Support Rating
7.9
(2 ratings)
-
(0 ratings)
User Testimonials
Cloudera Data Science WorkbenchNVIDIA RAPIDS
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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NVIDIA
NVIDIA RAPIDS drastically improves our productivity with near-interactive data science. And increases machine learning model accuracy by iterating on models faster and deploying them more frequently. It gives us the freedom to execute end-to-end data science and analytics pipelines.
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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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NVIDIA
  • Visualization
  • Deep learning pipeline
  • State of the art libraries
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Cons
Cloudera
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
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NVIDIA
  • Its not flexible and cost effective for all sizes of organizations.
  • I appreciate it has hassle-free integration.
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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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NVIDIA
No answers on this topic
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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NVIDIA
RAPIDS GPU accelerates machine learning to make the entire data science and analytics workflows run faster, also helps build databases and machine learning applications effectively. It also allows faster model deployment and iterations to increase machine learning model accuracy. The great value of money.
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Return on Investment
Cloudera
  • Paid off for demonstration purposes.
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NVIDIA
  • Efficient way to complete tasks
  • De-facto GPUs standard
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ScreenShots