Amazon SageMaker vs. Cloudera Data Science Workbench

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon SageMaker
Score 8.3 out of 10
N/A
Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.N/A
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
Pricing
Amazon SageMakerCloudera Data Science Workbench
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Amazon SageMakerData Science Workbench
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
Amazon SageMakerCloudera Data Science Workbench
Considered Both Products
Amazon SageMaker
Chose Amazon SageMaker
Amazon SageMaker is the best option for machine learning if you are already using the Amazon data science ecosystem. The software integrates nicely with MapReduce and most of the other Amazon tools. Additionally, MapReduce does a fairly good job of making the development of …
Data Science Workbench

No answer on this topic

Top Pros
Top Cons
Features
Amazon SageMakerCloudera Data Science Workbench
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Amazon SageMaker
-
Ratings
Cloudera Data Science Workbench
7.5
2 Ratings
12% below category average
Connect to Multiple Data Sources00 Ratings7.02 Ratings
Extend Existing Data Sources00 Ratings8.02 Ratings
Automatic Data Format Detection00 Ratings7.02 Ratings
MDM Integration00 Ratings8.02 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Amazon SageMaker
-
Ratings
Cloudera Data Science Workbench
7.6
2 Ratings
10% below category average
Visualization00 Ratings7.12 Ratings
Interactive Data Analysis00 Ratings8.02 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Amazon SageMaker
-
Ratings
Cloudera Data Science Workbench
7.8
2 Ratings
6% below category average
Interactive Data Cleaning and Enrichment00 Ratings7.02 Ratings
Data Transformations00 Ratings8.02 Ratings
Data Encryption00 Ratings8.02 Ratings
Built-in Processors00 Ratings8.02 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Amazon SageMaker
-
Ratings
Cloudera Data Science Workbench
7.6
2 Ratings
11% below category average
Multiple Model Development Languages and Tools00 Ratings8.02 Ratings
Automated Machine Learning00 Ratings7.01 Ratings
Single platform for multiple model development00 Ratings7.12 Ratings
Self-Service Model Delivery00 Ratings8.12 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Amazon SageMaker
-
Ratings
Cloudera Data Science Workbench
8.0
2 Ratings
7% below category average
Flexible Model Publishing Options00 Ratings8.12 Ratings
Security, Governance, and Cost Controls00 Ratings7.82 Ratings
Best Alternatives
Amazon SageMakerCloudera Data Science Workbench
Small Businesses
Google Cloud AI
Google Cloud AI
Score 8.4 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Google Cloud AI
Google Cloud AI
Score 8.4 out of 10
Mathematica
Mathematica
Score 8.3 out of 10
Enterprises
Dataiku
Dataiku
Score 7.9 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon SageMakerCloudera Data Science Workbench
Likelihood to Recommend
9.0
(6 ratings)
9.0
(3 ratings)
Support Rating
-
(0 ratings)
7.9
(2 ratings)
User Testimonials
Amazon SageMakerCloudera Data Science Workbench
Likelihood to Recommend
Amazon AWS
Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
Read full review
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.
Read full review
Pros
Amazon AWS
  • Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
  • Customization and easy to alter and change.
  • If you already are an Amazon user, you do not need to transition over to another software.
Read full review
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
Read full review
Cons
Amazon AWS
  • The UI can be eased up a bit for use by business analysts and non technical users
  • For huge amount of data pull from legacy solutions, the platform lags a bit
  • Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized
Read full review
Cloudera
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
Read full review
Support Rating
Amazon AWS
No answers on this topic
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.
Read full review
Alternatives Considered
Amazon AWS
Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
Read full review
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.
Read full review
Return on Investment
Amazon AWS
  • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
  • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
  • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
Read full review
Cloudera
  • Paid off for demonstration purposes.
Read full review
ScreenShots