1. leader badge
    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.
  2. leader badge
    The solution is very easy to use. It can send out large data amounts.
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    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.
  5. 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.
  6. 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.
  7. 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.
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  9. 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.
  10. 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.

Advice From The Community

Read answers to top Data Science Platforms questions. 475,705 professionals have gotten help from our community of experts.
There are many Data Science Platforms available. Which platform would you recommend that can handle large amounts of data?
author avatarZiad Chaudhry

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

author avatarAaronCooke
Real User

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

author avatarHyundong Lee

If you want to handle computer vision data, I recommend the Superb AI Suite. 

author avatarYogesh PARTE

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. 

author avatarreviewer1260093 (Professor of Health Services Research (now Emeritus) at a university with 1,001-5,000 employees)
Real User

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

author avatarEzzAbdelfattah
Real User

IBM SPSS Modeler

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?
author avatarGavin Robertson

Need to address basic data issues, e.g., quality, standardization and security, and MDM first, to obtain meaningful data visualization and single entity views, e.g., customer, patient and product. Ideally, a visualization tool should be able to interact with a backend actionable data catalog driven by data virtualization/federation either directly or through data provisioning. Power BI, QlikView and Tableau are excellent standard data visualization tools. Cambridge Intelligence's KeyLines is an excellent interactive graph visualization tool.

author avatarJorge Barroso

In my case, I can recommend Power BI, that works very well with a lot of database. It shows very good visualization graphs that allows to create many dashboards easily and connect with many data sources that can work very good to present, share and compare data thought the company and with users.

author avatarWillie Jacobs
Real User

We have been using Qlik Sense for the past 2 years and purchased but never really used Qlik View before that. We have used excel extensively and seen demos and tried Power BI and looked at demos for a couple of other BI tools.

We settled on using Qlik Sense as our Reporting, BI and Analytics tool due a very successful proof of concept delivered by our Qlik consultants.

Qlik Sense gives us the ability to visualize our data in various ways from simple bar and line charts or combined to scatter plots, mekko charts, funnel chart, pie charts, gauge charts and KPI items. Visualization options include table and pivot table that can be utilized to display detailed data. Visualizations also include a map chart that can be used to visualize various map layers with to display movement, density, are and points. 
This has been extremely valuable being from a logistics company.

I would therefore recommend Qlik Sense for the best visualization capabilities.

author avatarPeter Eerdekens
Real User

QlikSense. The associative analytics engine makes it kind of child's play to combine multi-source data and in combination with the augmented intelligence features QlikSense helps to create analytics and visualizations faster.

author avatarreviewer1066977 (Solution Architect/Technical Manager - Business Intelligence at a tech services company with 5,001-10,000 employees)
Real User

Now a days lot of visualization tools coming in the market, its difficult for anyone to choose from these variety of tools. However there can be various parameters which will help choose right set of Visualization tool for your requirements.

1. User Friendliness

2. Self Service Capability

3. Connectivity / compatibility with different systems that are available in the market

4. Compatibility with Cloud service providers

5. Relational, big-data systems and data lake connections, AI-ML and predictive analytics capabilities

6. License Cost 

I would recommend Power BI and Tableau as they provide lot of features and visualizations to choose from, with reasonable cost and connectivity with major systems.

author avatarTerry Dougal
Real User

For cost and capabilities, it' Looker.

I've used many softwares and this one seems have the best security, features and ease of use for the end user. 

author avatarVictor Feria

There are powerful options. QlikView, Tableu and PowerBi offers agile implementation.

Glen Green
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.
author avatarJorge Barroso

There is a lot of vendors that offers their data science platforms, but it depends on of what you call end-to-end vendors and if you write the Word really, well makes me think that you already test many of them. Data science platforms came from a variety of vendors like IBM, SAP, Microsoft, Domino Data labs, RapidMinder among others. First I suggest that you have a person or team ready to test these solutions, if not, remember to prepare some profiles with skills of programming and process design.

My recommendation is if you already work with IBM ask for their Data Science experience. In other case my suggestion is to try RapidMiner that seems to be very useful with a fluid interface for model deployment and could try Sas Enterprise Miner as the top of the model building and model deployment and appears as one of the leaders of these platforms.

I hope this was useful and regards.

author avatarChunqiangGong
Real User

KNIME or Alterxy is a good choice for a company to deploy AI applications.

It has:

1. light data processing like ETL,

2. AI modeling develop and deploy,

3. and output simple charts or output to databases for further use like API/BI/etc.

If you deploy in the cloud, you can also use the AWS Sagemaker or other cloud tools.

author avatarShilpa Prakash
Real User

There are many vendors offering end to end deployment with pros and cons. You can evaluate based on :
- On-prem vs cloud requirement
- Data volume that you want to process
- Do you already have ETL processes in place to extract the relevant data from diff sources?
- How are you planning to consume your ML output (API/dashboard/reports, etc)?
- Lastly, your ML algorithms that you intend to use and whether analyzing structured or unstructured data or both.

If you need further details, I will ask my presales to get in touch with you. Please provide me your contact information

author avatarPuneet Kumar

DataRobot for OnPrem
SageMaker for AWS

author avatarTrevor Legwinski

Another thing you need to be cognizant of is end-to-end platforms allow you to build and deploy models to production, that is ML 101, where the market is moving is building and scaling predictive applications for numerous business process and cases. Also many end-to-end platforms do not have the capabilities to deal with data drift, model retraining once it's in production and for more advanced use cases the capability for human-in-the-loop feedback to help retrain the model. A final thought I will put out there is explainability and interpretability are paramount today, you can build your models in open source, use these other tools to put them into production but you are going to have a gaping hole when someone comes to ask you, how did you build the model, what weights did you put on your features, how are you dealing with bias, etc. Majority of all platforms out there today, help you stitch together disparate open source solutions, but when you actually get into product-ionizing and scaling multiple business processes that are operationalized with machine learning they don't work.

author avatarTrevor Legwinski

The current issue today with the majority of DS platforms is they are based on disparate open-source libraries, or you need 5-6 different tools to build your end-to-end ML workflow, most have never seen production either.

At BigML we've been around for 10+ years were the first to market with an MLaaS platform and can help you and your team accomplish true end-to-end ML (source > dataset> model > predictions > production) all in a singular platform, we work with many clients in your space, and would be happy to talk with you. You can even sign up for our platform for free and take it for a spin.

author avatarOvidiu-Catalin Mucenic

One potential solution might be the SAS platform https://www.sas.com/en_us/software/platform.html

author avatarAnthony Field (NetSuite)

As others have said, many options but add Dataiku, H2Oi, Alteryx, and Databricks to your list.

See more Data Science Platforms questions »
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475,705 professionals have used our research since 2012.