Popular Comparisons Technical support is very helpful.
Data processing is most valuable. It is one of the fastest data blockers out there in the market, which is a fascinating thing about Alteryx.
Popular Comparisons The solution is very easy to use.
It can send out large data amounts.
Popular Comparisons KNIME is quite scalable, which is one of the most important features that we found.
It can handle an unlimited amount of data, which is the advantage of using Knime.
Popular Comparisons It's good for citizen data scientists, but also, other people can use Python or .NET code.
The solution is very easy to use, so far as our data scientists are concerned.
Popular Comparisons You can quickly build models because it does the work for you.
Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful.
Popular Comparisons The documentation is excellent and the solution has a very large and active community that supports it.
It's interesting. It's user friendly. That's what makes it outstanding among the others. It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science.
Popular Comparisons The best part of RapidMiner is efficiency.
Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations.
Popular Comparisons I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks.
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Advice From The Community
Read answers to top Data Science Platforms questions. 475,705 professionals have gotten help from our community of experts.![]() | Rony_Sklar Community Manager at IT Central Station |
There are many Data Science Platforms available. Which platform would you recommend that can handle large amounts of data?
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![]() | Rony_Sklar Community Manager at IT Central Station |
There are so many data science platforms to choose from. Which platform would you recommend to enterprise-level companies that want flexible and powerful data visualization capabilities to drill down into the data? What makes the solution that you recommend a better choice than others?
![]() | Glen Green Sr. Project Manager at a manufacturing company |
I have experience working as a senior integration architect for AI/ML enablement for a manufacturing company with 10,000+ employees.
We are currently evaluating data science platforms. Which vendor offers an end-to-end solution that really works from features management to model deployment?
Thanks! I appreciate the help.
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: March 2021.
475,705 professionals have used our research since 2012.
DakaIku is a great general purpose data science platform for both supervised and unsupervised learning. It handles Big Data very well.
Sparkcognition's Darwin product can handle very large data sets.
If you want to handle computer vision data, I recommend the Superb AI Suite.
https://www.superb-ai.com/
The question also needs to specify which domain, what kind of data and public or private platforms.
For structured/tabular data driverless AI / H20.ai sparkling water is my preferred platform.
My experience has not been on large scale systems. Not even multi-terabytes. My mult-megabytes would not help. Sorry!
IBM SPSS Modeler