We performed a comparison between Dataiku Data Science Studio, KNIME, and SAS Enterprise Miner based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."Data Science Studio's data science model is very useful."
"The most valuable feature is the set of visual data preparation tools."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The solution is quite stable."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"We have found KNIME valuable when it comes to its visualization."
"It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea."
"Clear view of the data at every step of ETL process enables changing the flow as needed."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"We can deploy the solution in a cluster as well."
"It has allowed us to easily implement advanced analytics into various processes."
"The solution is able to handle quite large amounts of data beautifully."
"Good data management and analytics."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"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."
"he solution is scalable."
"The solution is very good for data mining or any mining issues."
"The technical support is very good."
"I like the way the product visually shows the data pipeline."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The ability to have charts right from the explorer would be an improvement."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"The ability to handle large amounts of data and performance in processing need to be improved."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."
"From the point of view of the interface, they can do a little bit better."
"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"The data visualization part is the area most in need of improvement."
"The user interface of the solution needs improvement. It needs to be more visual."
"The visualization of the models is not very attractive, so the graphics should be improved."
"The ease of use can be improved. When you are new it seems a bit complex."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"The initial setup is challenging if doing it for the first time."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The solution is much more complex than other options."
"Virtualization could be much better."