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Anaconda vs Cloudera Data Science Workbench comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Anaconda
Ranking in Data Science Platforms
11th
Average Rating
8.2
Reviews Sentiment
7.4
Number of Reviews
19
Ranking in other categories
No ranking in other categories
Cloudera Data Science Workb...
Ranking in Data Science Platforms
23rd
Average Rating
7.0
Reviews Sentiment
6.9
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Anaconda is 2.1%, down from 2.2% compared to the previous year. The mindshare of Cloudera Data Science Workbench is 1.3%, down from 1.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Rohan Sharma - PeerSpot reviewer
Provides all the frameworks and makes it easy to create environments for multiple projects
The best thing is that it provides all the frameworks and makes it easy to create environments for multiple projects using Anaconda. It is easy for a beginner to learn to use Anaconda. Comparatively, it is easier than using virtual environments or other environments because of the Conda environment. However, there are many things in Anaconda that people need to be aware of, so it can be challenging.
Ismail Peer - PeerSpot reviewer
Useful for data science modeling but improvement is needed in MLOps and pricing
If you don't configure CDSW well, then it might be not useful for you. Deploying the tool can vary in complexity, but most of the time, it's relatively simple and straightforward. Triggering a job from data to production is easy, as the platform automates the deployment process. However, ensuring optimal resource allocation is essential for smooth operations.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Anaconda is an open-source platform that can integrate numerous other kits and models in one place."
"The documentation is excellent and the solution has a very large and active community that supports it."
"The virtual environment is very good."
"The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors."
"It provides a unified platform where you can install Jupyter, Python Spider, and other related tools without needing separate installations."
"The notebook feature is an improvement over RStudio."
"It has a lot of functionality available, supports many libraries, and the developers are continually improving it."
"The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code."
"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The Cloudera Data Science Workbench is customizable and easy to use."
 

Cons

"I think that the framework can be improved to make it easier for people to discover and use things on their own."
"The solution would benefit from offering more automation."
"The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform."
"The process could be streamlined as the number of actions needed to deploy is quite large compared to other tools."
"When you install Anaconda for the first time, it's really difficult to update it."
"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."
"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."
"Anaconda should be optimized for RAM consumption."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
 

Pricing and Cost Advice

"The tool is open-source."
"The product is open-source and free to use."
"My company uses the free version of the tool. There is also a paid version of the tool available."
"Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks."
"The licensing costs for Anaconda are reasonable."
"The product is expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
20%
Computer Software Company
9%
Government
8%
Manufacturing Company
8%
Financial Services Firm
33%
Manufacturing Company
10%
Healthcare Company
8%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Anaconda?
The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.
What is your experience regarding pricing and costs for Anaconda?
Anaconda does not require a pricing structure, and it is available as an open-source tool. The features of Python, Jupyter, and others are free to use.
What needs improvement with Anaconda?
Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvement as it can impact performance.
What do you like most about Cloudera Data Science Workbench?
I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy...
What needs improvement with Cloudera Data Science Workbench?
The tool's MLOps is not good. It's pricing also needs to improve.
What is your primary use case for Cloudera Data Science Workbench?
We have different use cases. Our banking use case uses machine learning to identify customer life events and recommend the best-suited card products. These machine-learning models are deployed in o...
 

Also Known As

No data available
CDSW
 

Overview

 

Sample Customers

LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
IQVIA, Rush University Medical Center, Western Union
Find out what your peers are saying about Anaconda vs. Cloudera Data Science Workbench and other solutions. Updated: April 2025.
849,190 professionals have used our research since 2012.