2020-08-18T04:51:00Z

What Data Science Platform is best suited to a large-scale enterprise?

Rony_Sklar - PeerSpot reviewer
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9 Answers

ZC
Real User
2020-10-15T16:18:55Z
Oct 15, 2020

DakaIku is a great general purpose data science platform for both supervised and unsupervised learning. It handles Big Data very well.

AA
User
Aug 25, 2021

@Ziad Chaudhry I'd also vote for Dataiku, look at their cases https://www.dataiku.com/storie...

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AC
Real User
2020-08-18T12:47:29Z
Aug 18, 2020

Sparkcognition's Darwin product can handle very large data sets. 

Rony_Sklar - PeerSpot reviewer
Community Manager
Aug 19, 2020

Thanks for your input @AaronCooke ​:) 

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DG
Vendor
2021-08-26T12:58:55Z
Aug 26, 2021

Data science platform is a vague term.  


It all depends on what you wish to accomplish. Are you talking about fast databases, ETLs, a Machine Learning tool, integration with R or Python, Self-Service Data Visualization Tool, Collaboration? No size fits all...

JC
User
2021-08-26T03:42:21Z
Aug 26, 2021

Dataiku, Domino, RapidMiner are notable candidates for your purpose, I presume. 


It has been 2 years when I checked several vendors and made the list as candidates. They all support large-scale data manipulation for data analysis and machine learning development as a platform that can be used by many people in a collaborative way.

Laurence Moseley - PeerSpot reviewer
Real User
Top 10
2021-08-24T10:48:49Z
Aug 24, 2021

I suspect that I cannot answer this. I have used Knime and RapidMiner with data sets that have had up to about 80,000 rows and 1,500 columns and both have performed well. However, I doubt whether the questioner would classify my usage as "large amounts of data". If my usage is like theirs, then both packages can be recommended.


Both Knime and RapidMiner offer the facility to link with Python or R, and those languages have modules or methods which offer better performance on large data sets (multi-processing or using GPUs, etc.), so those combinations might serve their purpose. So, they might use, say, Knime for ease of use and, say, R for the excess power or RapidMiner and Python.

HL
User
2020-09-09T22:37:17Z
Sep 9, 2020

If you want to handle computer vision data, I recommend the Superb AI Suite. 
https://www.superb-ai.com/

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YP
User
2020-08-18T17:51:00Z
Aug 18, 2020

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. 

Rony_Sklar - PeerSpot reviewer
Community Manager
Aug 19, 2020

@Yogesh PARTE ​Good point - this is a more general question, but I do agree that it's easier to make recommendations with more details. Would you mind sharing more about why H20.ai Sparkling Water is your preferred choice in this instance?

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LM
Real User
Top 5Leaderboard
2020-08-18T16:24:46Z
Aug 18, 2020

My experience has not been on large scale systems. Not even  multi-terabytes. My mult-megabytes would not help. Sorry!

EzzAbdelfattah - PeerSpot reviewer
Real User
Top 5Leaderboard
2020-08-18T10:14:53Z
Aug 18, 2020

IBM SPSS Modeler

WA
Real User
Feb 26, 2021

@EzzAbdelfattah IMHO it's pretty much limited and outdated to handle with the latest frameworks features,

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