Anaconda vs Cloudera Data Science Workbench comparison

Anaconda and Cloudera are both solutions in the Data Science Platforms category. Anaconda is ranked #11 with an average rating of 8.8, while Cloudera is ranked #22 with an average rating of 6.0. Anaconda holds a 2.1% mindshare in Data Science Platforms, compared to Cloudera’s 1.5% mindshare. Additionally, 95% of Anaconda users are willing to recommend the solution, compared to 66% of Cloudera users who would recommend it.

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Executive SummaryUpdated on Dec 5, 2024

Anaconda and Cloudera Data Science Workbench are competing in the data science platform space. Anaconda appears to be more favorable in terms of pricing and ease of use, whereas Cloudera Data Science Workbench offers superior scalability and enterprise-level features.

Features: Anaconda offers a comprehensive package manager, environment management, and user-friendly interface designed for individual productivity. Cloudera Data Science Workbench provides distributed computing support, enhanced collaboration capabilities, and enterprise security features suited for large teams and complex project environments.

Ease of Deployment and Customer Service: Anaconda's deployment process is straightforward, supplemented by extensive documentation and support for smaller teams or individual users. Cloudera Data Science Workbench, though offering a more intricate deployment process, delivers top-tier support for enterprise-scale implementations, ensuring robust operability for large organizations.

Pricing and ROI: Anaconda is an economical choice, particularly for small to medium-sized projects, thanks to its open-source foundation. Cloudera Data Science Workbench necessitates a more substantial investment but offers strong ROI through its capacity to handle extensive data sets and facilitate enterprise collaboration. Anaconda is more cost-efficient for smaller operations, whereas Cloudera's features validate its higher price point for larger enterprises.

To learn more, read our detailed Anaconda vs. Cloudera Data Science Workbench Report (Updated: December 2024).

Buyer's Guide

Anaconda vs. Cloudera Data Science Workbench

December 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of December 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.7%, up from 2.7% compared to the previous year. The mindshare of Anaconda is 2.1%, down from 2.3% compared to the previous year. The mindshare of Cloudera Data Science Workbench is 1.5%, down from 1.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Md Masudul Hassan

Agricultural Finance and IT Researcher at Sher-e-Bangla Agricultural University

Comprehensive data analysis capabilities with a user-friendly interface, providing an efficient and reliable platform for researchers and analysts

I believe that offering short-term SPSS licenses, perhaps when customer sourcing is available, could make it more affordable. These licenses shouldn't include features tailored for universities or large sales organizations. Instead, they could offer discounts or additional facilities for smaller entities to access the software. In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options. For example, offering basic features to the first hundred users can help them become familiar with the software and its capabilities. This approach encourages users to upgrade to higher tiers as they become more experienced and require additional functionality.

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Rohan Sharma

AI/ML Co-Lead at Developer Student Clubs - GGV

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.

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Ismail Peer

Program Management Lead Advisor at Unionbank Philippines

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.

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Quotes from Members

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

Pros

"One feature I found very valuable was the analysis of variance (ANOVA)."

"It is perfectly adequate if all you need are the results and not the trail of evidence."

"You can quickly build models because it does the work for you."

"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."

"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."

"It is a modeling tool with helpful automation."

"IBM SPSS Statistics depends on AI."

"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."

More IBM SPSS Statistics pros

"The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results."

"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."

"Voice Configuration and Environmental Management Capabilities are the most valuable features."

"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."

"The solution is stable."

"With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages."

"The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."

More Anaconda pros

"The Cloudera Data Science Workbench is customizable and easy to use."

"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."

Cons

"There is a learning curve; it's not very steep, but there is one."

"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."

"The design of the experience can be improved."

"I would like SPSS to improve its integration with other data-filing IBM tools. I also think its duration with data, utilization, and graphics could be better."

"It could provide even more in the way of automation as there are many opportunities."

"The solution needs more planning tools and capabilities."

"The statistics should be more self-explanatory with detailed automated reports."

"SPSS is a tool that's been around since the late 60s, and it's the universal worldwide standard for quantitative social science data analysis. That said, it does seem a bit strange to me that the graphical output functions are so clunky after all these years. The output of charts and graphs that SPSS produces is hideous."

More IBM SPSS Statistics cons

"Anaconda consumes a significant amount of processing memory when working on it."

"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."

"It also takes up a lot of space."

"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."

"I think that the framework can be improved to make it easier for people to discover and use things on their own."

"Anaconda can't handle heavy workloads."

More Anaconda cons

"The tool's MLOps is not good. It's pricing also needs to improve."

"Running this solution requires a minimum of 12GB to 16GB of RAM."

Pricing and Cost Advice

"More affordable training for new staff members."

"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."

"We think that IBM SPSS is expensive for this function."

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"The price of IBM SPSS Statistics could improve."

"I rate the tool's pricing a five out of ten."

"It's quite expensive, but they do a special deal for universities."

More IBM SPSS Statistics pricing and cost advice

"Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks."

"The product is open-source and free to use."

"The licensing costs for Anaconda are reasonable."

"My company uses the free version of the tool. There is also a paid version of the tool available."

"The tool is open-source."

"The product is expensive."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

Financial Services Firm

19%

Computer Software Company

10%

University

Manufacturing Company

Financial Services Firm

36%

Manufacturing Company

12%

Healthcare Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

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What is your experience regarding pricing and costs for IBM SPSS Statistics?

The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...

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What needs improvement with IBM SPSS Statistics?

IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...

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What do you like most about Anaconda?

The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using...

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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, Ju...

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What needs improvement with Anaconda?

Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvem...

See all answers

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'...

See all answers

What needs improvement with Cloudera Data Science Workbench?

The tool's MLOps is not good. It's pricing also needs to improve.

See all answers

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 recommen...

See all answers

Comparisons

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Compared 16% of the time

Alteryx vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 11% of the time

IBM Watson Studio vs IBM SPSS Statistics

Compared 2% of the time

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Databricks vs Anaconda

Compared 42% of the time

Microsoft Power BI vs Anaconda

Compared 12% of the time

Amazon SageMaker vs Anaconda

Compared 12% of the time

Microsoft Azure Machine Learning Studio vs Anaconda

Compared 9% of the time

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Databricks vs Cloudera Data Science Workbench

Compared 31% of the time

Amazon SageMaker vs Cloudera Data Science Workbench

Compared 30% of the time

Dataiku vs Cloudera Data Science Workbench

Compared 14% of the time

Microsoft Azure Machine Learning Studio vs Cloudera Data Science Workbench

Compared 9% of the time

Google Cloud Datalab vs Cloudera Data Science Workbench

Compared 9% of the time

More Cloudera Data Science Workbench Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

December 2024

Download IBM SPSS Statistics product report

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Anaconda

December 2024

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Buyer's Guide

Data Science Platforms

December 2024

Download Cloudera Data Science Workbench product report

Also Known As

SPSS Statistics

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CDSW

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IBM

Anaconda

Cloudera

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.

Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination allows you to do everything you want from BI to advanced modeling on complex Big Data

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis.

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone

IQVIA, Rush University Medical Center, Western Union

Buyer's Guide

Anaconda vs. Cloudera Data Science Workbench

December 2024

Free Report: Anaconda vs. Cloudera Data Science Workbench

Find out what your peers are saying about Anaconda vs. Cloudera Data Science Workbench and other solutions. Updated: December 2024.

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