Cloudera Data Science Workbench vs. HPE Data Fabric

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
HPE Data Fabric
Score 9.4 out of 10
N/A
HPE Data Fabric (formerly MapR, acquired by HPE in 2019) is a software-defined datastore and file system that simplifies data management and analytics by unifying data across core, edge, and multicloud sources into a single platform.N/A
Pricing
Cloudera Data Science WorkbenchHPE Data Fabric
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Data Science WorkbenchHPE Data Fabric
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 WorkbenchHPE Data Fabric
Features
Cloudera Data Science WorkbenchHPE Data Fabric
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Cloudera Data Science Workbench
7.5
Ratings
11% below category average
HPE Data Fabric
-
Ratings
Connect to Multiple Data Sources7.00 Ratings00 Ratings
Extend Existing Data Sources8.00 Ratings00 Ratings
Automatic Data Format Detection7.00 Ratings00 Ratings
MDM Integration8.00 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Cloudera Data Science Workbench
7.6
Ratings
10% below category average
HPE Data Fabric
-
Ratings
Visualization7.10 Ratings00 Ratings
Interactive Data Analysis8.00 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Cloudera Data Science Workbench
7.8
Ratings
4% below category average
HPE Data Fabric
-
Ratings
Interactive Data Cleaning and Enrichment7.00 Ratings00 Ratings
Data Transformations8.00 Ratings00 Ratings
Data Encryption8.00 Ratings00 Ratings
Built-in Processors8.00 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Cloudera Data Science Workbench
7.6
Ratings
10% below category average
HPE Data Fabric
-
Ratings
Multiple Model Development Languages and Tools8.00 Ratings00 Ratings
Automated Machine Learning7.00 Ratings00 Ratings
Single platform for multiple model development7.10 Ratings00 Ratings
Self-Service Model Delivery8.10 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Cloudera Data Science Workbench
8.0
Ratings
6% below category average
HPE Data Fabric
-
Ratings
Flexible Model Publishing Options8.10 Ratings00 Ratings
Security, Governance, and Cost Controls7.80 Ratings00 Ratings
Best Alternatives
Cloudera Data Science WorkbenchHPE Data Fabric
Small Businesses
Jupyter Notebook
Jupyter Notebook
Score 9.4 out of 10

No answers on this topic

Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Enterprises
Posit
Posit
Score 10.0 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 7.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Cloudera Data Science WorkbenchHPE Data Fabric
Likelihood to Recommend
9.0
(0 ratings)
7.2
(0 ratings)
Support Rating
7.9
(0 ratings)
-
(0 ratings)
User Testimonials
Cloudera Data Science WorkbenchHPE Data Fabric
Likelihood to Recommend
  • If you already have a Cloudera partnership and a cluster, having this is a no brainer.
  • It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
  • With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.
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If you need Hadoop and just need raw speed for I/O and have a Hadoop savvy group of engineers who don't need/like web UIs, then MapR is a great fit for you. If you are new to Hadoop or have DevOps folks that are not Hadoop gurus, choosing MapR as your Hadoop vendor will have a steeper learning curve as you will need to do more training and build more admin consoles for them.
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Pros
  • Enterprise grade security.
  • Self-service analytics platform.
  • Popular programming support.
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  • MapR allows easy integration with HBase and MapR DB.
  • Easy trial server setup for product testing.
  • Excellent training program to help new users get up-to-date with MapR and related products.
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Cons
  • Not as great as RStudio; lacks some features when compared with it
  • It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product
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  • I think MapR's main problem is name recognition. Hortonworks and Cloudera both are big names in the industry, but their deployment mechanisms are a little more difficult to use, especially when trying to fully automate it's deployment.
  • Documentation could always be better. But really, if that's your main weakness, it's everybody's weakness.
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Support Rating
It is expensive and difficult to install and maintain.
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No answers on this topic
Alternatives Considered
Since our organization had already implemented Cloudera Data Platform as our Big Data Warehouse platform, implementing CDSW as the go-to Analytic and Data Science Platform is the most logical and cost-effective decision to make. It integrates seamlessly with our CDH clusters and it also provides enterprise-grade security for on-premise implementation.
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Hortonworks and Cloudera are both sort of hacky. We have to do a lot of extra steps to automate those two. MapR has far fewer issues and doesn't force you into a once size fits all deployment scenario. There are multiple ways to deploy and some are more amenable to automation, MapR just has that in spades
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Return on Investment
  • Paid off for demonstration purposes.
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  • Less manual intervention for maintaining a cluster.
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ScreenShots