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dotData VS PurpleCube

Compare dotData VS PurpleCube and see what are their differences

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dotData logo dotData

dotData is a data automation platform that enables enterprises to operationalize data science and machine learning.

PurpleCube logo PurpleCube

PurpleCube is a cloud-based AI and ML data analytics platform that allows users to easily and quickly analyze complex data sets without requiring any data science or coding skills.
  • dotData Landing page
    Landing page //
    2023-06-12
  • PurpleCube Landing page
    Landing page //
    2023-09-20

Category Popularity

0-100% (relative to dotData and PurpleCube)

dotData

PurpleCube

Technical Computing
47 47%
Technical Computing
53% 53
Development
42 42%
Development
58% 58
Business & Commerce
42 42%
Business & Commerce
58% 58
Data Dashboard
29 29%
Data Dashboard
71% 71

User comments

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What are some alternatives?

When comparing dotData and PurpleCube, you can also consider the following products

IBM ILOG CPLEX Optimization Studio - IBM ILOG CPLEX Optimization Studio is an easy-to-use, affordable data analytics solution for businesses of all sizes who want to optimize their operations.

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

RapidMiner Studio - Visual workflow designer for predictive analytics that brings data science and machine learning to everyone on the analytics team

Tibco Data Science - Data science is a team sport. Data scientists, citizen data scientists, business users, and developers need flexible and extensible tools that promote collaboration, automation, and...

Pyramid Analytics - Pyramid Analytics provides business intelligence software that delivers data-driven insights for organizations with advanced analytics and data visualizations.

AIXON - AIXON is an AI-powered data science solution that enables data scientists of all levels of experience to build machine learning models and deploy them into production with less code and without the need for a data science team.

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