Anaconda vs. Cloudera Data Science Workbench

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Anaconda
Score 8.6 out of 10
N/A
Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.
$0
per month
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
AnacondaCloudera Data Science Workbench
Editions & Modules
Free Tier
$0
per month
Starter Tier
$9
per month
Business Tier
$50
per month per user
Enterprise Tier
60.00+
per month per user
No answers on this topic
Offerings
Pricing Offerings
AnacondaData Science Workbench
Free Trial
NoNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
AnacondaCloudera Data Science Workbench
Top Pros
Top Cons
Features
AnacondaCloudera Data Science Workbench
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Anaconda
9.4
24 Ratings
11% above category average
Cloudera Data Science Workbench
7.5
2 Ratings
12% below category average
Connect to Multiple Data Sources9.722 Ratings7.02 Ratings
Extend Existing Data Sources8.923 Ratings8.02 Ratings
Automatic Data Format Detection9.621 Ratings7.02 Ratings
MDM Integration9.514 Ratings8.02 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Anaconda
9.2
24 Ratings
9% above category average
Cloudera Data Science Workbench
7.6
2 Ratings
10% below category average
Visualization9.624 Ratings7.12 Ratings
Interactive Data Analysis8.923 Ratings8.02 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Anaconda
9.4
25 Ratings
13% above category average
Cloudera Data Science Workbench
7.8
2 Ratings
6% below category average
Interactive Data Cleaning and Enrichment8.823 Ratings7.02 Ratings
Data Transformations9.625 Ratings8.02 Ratings
Data Encryption9.719 Ratings8.02 Ratings
Built-in Processors9.520 Ratings8.02 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Anaconda
9.3
23 Ratings
9% above category average
Cloudera Data Science Workbench
7.6
2 Ratings
11% below category average
Multiple Model Development Languages and Tools9.622 Ratings8.02 Ratings
Automated Machine Learning8.821 Ratings7.01 Ratings
Single platform for multiple model development8.923 Ratings7.12 Ratings
Self-Service Model Delivery9.618 Ratings8.12 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Anaconda
9.5
20 Ratings
10% above category average
Cloudera Data Science Workbench
8.0
2 Ratings
7% below category average
Flexible Model Publishing Options9.520 Ratings8.12 Ratings
Security, Governance, and Cost Controls9.519 Ratings7.82 Ratings
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AnacondaCloudera Data Science Workbench
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Score 8.3 out of 10
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All AlternativesView all alternativesView all alternatives
User Ratings
AnacondaCloudera Data Science Workbench
Likelihood to Recommend
9.5
(37 ratings)
9.0
(3 ratings)
Likelihood to Renew
7.0
(1 ratings)
-
(0 ratings)
Usability
9.0
(2 ratings)
-
(0 ratings)
Support Rating
8.9
(9 ratings)
7.9
(2 ratings)
User Testimonials
AnacondaCloudera Data Science Workbench
Likelihood to Recommend
Anaconda
As a Data Analyst, it is my job to analyze large datasets using complex mathematical models. Anaconda provides a one-stop destination with tools like PyCharm, Jupyter, Spyder, and RStudio. One case where it is well suited is for someone who has just started his/her career in this field. The ability to install Anaconda requires zero to little skills and its UI is a lot easier for a beginner to try. On the other hand, for a professional, its ability to handle large data sets could be improved. From my experience, it has happened a lot that the system would crash with big files.
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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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Pros
Anaconda
  • It provides easy access to software like Jupyter, Spyder, R and QT Console etc.
  • Easy installation of Anaconda even without much technical knowledge.
  • Easy to navigate through files in Jupyter and also to install new libraries.
  • R Studio in Anaconda is easy to use for complex machine learning algorithms.
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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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Cons
Anaconda
  • Although I have generally had positive experiences with Anaconda, I have had trouble installing specific python libraries. I tried to remedy the solution by updating other packages, but in the end, things got really messed up, and I ended up having to uninstall and reinstall a total of about 4 times over the past 2 years.
  • If you have the free version of Anaconda, there is not much support. Googling questions and error messages are helpful, but there were times when I wished I would have been able to ask technical support to help me troubleshoot issues.
  • There were a few times when I tried to install tensorflow and tensorboard via Anaconda on a PC, but I could not get them to install properly. Anaconda allows you to create 'environments' , which allow you to install specific versions of python and associated libraries. You can keep your environments separate so they do not conflict with one another. Anyway, I ended up having to create several 'conda envrionments' just so I could use tensforflow/tensorboard and a few other utilities to avoid errors. This was somewhat annoying, because every time I wanted to run a specific model, I'd have to open up the specific conda environment with the appropriate python libraries.
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Cloudera
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
Read full review
Likelihood to Renew
Anaconda
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
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Cloudera
No answers on this topic
Usability
Anaconda
The interface is an easy to use command-line interface, or a GUI for launching and/or discovering different parts of the system.
Read full review
Cloudera
No answers on this topic
Support Rating
Anaconda
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
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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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Alternatives Considered
Anaconda
ANACONDA VS Alteryx Analytics: Even though I find Alteryx to be an excellent tool for managing extremely massive data, Anaconda is much better and easy for analytics. Anaconda VS. MicroStrategy Analytics: Compared with Anaconda, MicroStrategy Analytics is very difficult to use and counter-intuitive Anaconda VS. Power BI For Office 365: One of the main advantages of BI for Office 364 is its capacity to data connectivity. However, it's very hard to edit data connections, once BI for Office is deployed in other platforms
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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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Return on Investment
Anaconda
  • Positive: Lower maintenance cost compared to other tools on the market
  • Positive: Ease in hiring professionals already accustomed to the tool in the job market
  • Positive: Projects are portable, allowing you to share projects with others and execute projects on different platforms, reducing deployment costs
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Cloudera
  • Paid off for demonstration purposes.
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ScreenShots