The solution allows for sharing model designs and model operations with other data analysts. Other solutions such as SAS, R, and Python consist of just the script which is difficult to share. 

The solution is a low-code platform which reduces data mining time and its platform includes a clickable icon for linking script. 

I used to be a Pascal programmer, and then I did a bit of Python. It does many of the things that I would've had to do in code, but does so without using code. I don't think it does everything, but it does most of what I need to do.

It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional.

For example, I have one data set whereby all of the data is encoded and there was one variable called opinion or something like that and it had codes for what the topic was, which was being discussed, whether it was positive or negative, whether it was strongly worded or weakly worded, and so many other things like that.

I had to transfer those into columns, like sentiment, the strength of sentiments, topic being discussed. I had to split it up into columns, and I could do that very easily, like simple JavaScript, in their column expressions.

It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.

They are also very careful with things like lab variants and issued variants because they have some labs that develop nodes, and new chunks of code which are represented as an icon. They make it very clear that those lab ones are not fully tested, and they're very glad to get comments back if you have problems.

I haven't had that difficulty myself. They seem to be aware that they have the community there as their testing base, and they seem not to be embarrassed about that. They will tell you when they go wrong and try to put it right.

There are several valuable features. First, it's a free product. Second, its speed due to the no-code approach. And third, its a comprehensive library of nodes that covers almost anything you need.

Automation is most valuable. It allows me to automatically download information from different sources, and once I create a workflow, I can apply it anytime I want. So, there is efficiency at the same time.

It's like an Excel on drugs. It's more powerful than Excel, and it allows me to do macros easily.

Since KNIME is a no-code platform, it is easy to work with. You don't have to write any codes and try to fix all the bits and pieces of coding or the intricacies of the programming language. Instead, getting a quick data prep or big data and eventually running it through your hypothesis is pretty fast. It's not ideal for huge data sets worth gigabytes, but it's okay since very few people have big data sets.

KNIME is easy to learn. You can code with KNIME using the visual coding platform if you know how to code. If you're working in an account management or financial department, you can use KNIME to work with a huge amount of data quickly. You can use KNIME to schedule your workflows, send emails, and write codes.

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.

The most valuable is the ability to seamlessly connect operators without the need for extensive programming.

The most valuable feature of the solution stems from the fact that it is a user-friendly tool where a person doesn't have to get involved with codes since you just need to drag the nodes to create your model, which is a very easy process for me.

One of the greatest advantages of KNIME is that it can be used by those without any coding experience. Even those with no coding background can use it. 

KNIME can be used by people without coding experience. It can be used by people who don't have an IT background and don't have coding knowledge. This is different from Python or R, which require coding experience to use.

There are many valuable features in KNIME. One of the most useful aspects is that it can read a wide variety of data file types. Additionally, the ability to manipulate data, such as deleting rows or columns, is very helpful. I also use many of the nodes for analyzing data, such as doing frequencies and cross tabs. I have used it for machine learning tasks, like decision trees and random forests. It also has neural network capabilities, but I am not an expert in that area, so I cannot comment on it.

It's a very powerful and simple tool to use.

KNIME has met all of my needs so far. It has excellent data visualization capabilities. Additionally, it has a text analysis package, which I haven't used. However, I am satisfied with the features currently available and it has a strong support community.

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.

I've tried to utilize KNIME to the fullest extent possible to replace Excel. Our company has been heavily reliant on Excel for generating reports and performing data transformations. With KNIME, I've been able to combine data from Excel, SQL Server, and various other resources efficiently.

What I like most about KNIME is that it's user-friendly. It's a low-code, no-code tool, so students don't need coding knowledge. You can make use of different kinds of nodes. KNIME even has a good description of each node.

It's also helpful to learn more about the different concepts in KNIME, such as data mining, neural networks, and other visualization nodes for generation. You can gain a lot of insight from the tool as a data scientist.

KNIME is very fast and scalable. There are a lot of connectors available in KNIME.

The solution is the best data science tool if you gain in-depth knowledge about its functions.

The features that I find most valuable are:

1. The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes.
2. Unlimited volume of data; you are only limited by the machine you run on.
3. Python and R integration.
4. Predictive functionality and text analytics. If it is not enough then you can use custom Python and R scripts.
5. Looping functionality.
6. Variables allow you to parameterize your flows.
7. Run one node at a time, which is something that Alteryx users dream of doing.
8. Managing (collapsing) sub-flows, which is another thing that Alteryx container users also dream of.

I believe that some individuals may not be skilled programmers, and this is where the agenda comes in. 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.   

We have found KNIME valuable when it comes to its visualization.

Overall KNIME serves its purpose and does a good job.

From a user-friendliness perspective, it's a great tool.

• Easy to connect with every database: We use queries from SQL, Redshift, Oracle.
• Easy to have a clear view of the data at every single step of the ETL process, with the consequent possibility of changing the flow according to your needs.

I like the visual features and that we can connect using a pixel or an icon for the sources and controls. KNIME is easy to use, it's stable, powerful, and has a lot of features.

All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function.

Technical support has been extremely responsive so far.

The solution has a very strong and supportive community that shares information and helps each other troubleshoot.

The solution is very stable.

The initial setup is pretty simple and straightforward.

It can handle an unlimited amount of data, which is the advantage of using Knime.

It already has algorithms included.

The solution is good for teaching, since there is no need to code. When teaching, the work flow, the process, is easy to explain to students. They understand that there are four steps in data mining on the machine learning project. Data must be injected and cleaned, after which engineering features must be made. Finally, the model must be created and deployed. Both KNIME and RapidMiner have these capabilities.

I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data.

I've been using some of the analytics and photo manipulation features. I'm a teacher, and I've been using KNIME in my university.

It's a huge tool with machine learning features as well.

The solution allows one to do many things, including data preparation. I was able to apply basic algorithms through just dragging and dropping. This in contrast to Python and other solutions, which involve much coding. 

KNIME has a good set of features for data analytics.

The sampling features are some of the most important ones for us.

It is a complete data science platform and especially good at scaling up data preparation and wrapping. It provides a large numbers of algorithms to look at data from different angles

The most valuable part of the solution is the machine learning part. The second feature that we use most is big data connectivity. When we deployed the architecture, we directed our IDS (Intrusion Detection System) server to where the big data will be on our servers. Then we needed some kind of basic machine learning and obviously. After that, we connected it with Tableau visualization. Now we are writing the big data part of our solution along with the overall machine learning. These two parts will be the most important for our business going forward.  

I think also connectivity with hybrid databases and also integration with languages like Python are great advantages to what we are seeking to do in our environment. We have been using these features extensively and we find them to be very valuable in achieving what we hoped to achieve with the tool.  

• Easy ETL operations
• Rich algorithm set
• Integrated with other languages like R, Python, and Java.
• Works together with other technologies like DeepLearning4j, H2O.ai, D3.js, and Weka.
• Ease of use and being a performant solution.
• Continues development and wide community support

We find KNIME useful and convenient for data preparation.

It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript. 

The data analytics capabilities in KNIME are excellent. It's not just a statistical ETL tool. We can go deeper and do various types of tasks beyond straight analytics.

The product is open-source and therefore free to use.

The solution offers lots of different options.

It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop.

Easy to use nodes for ETL processes. This is because, in many cases, I usually transform the data before the main task even when the data is from a structured database.

The nicest part of KNIME is that the designer tool is free.

This solution is easy to use and it can be used to create any kind of model.

Most important, it is open-source. Next is the ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database.

• The very easy-to-use visual interface
• Help functions and clear explanations of the functionalities and the used algorithms
• Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis

For inexperienced analysts or data scientists, it is a very easy tool to take your first steps in modeling and analytics.

• Visual workflow creation
• Workflow variables (parameterisation)
• Automatic caching of all intermediate data sets in the workflow
• Scheduling with the server

It's a coding-less opportunity to use AI. This is the major value for me.

Data preparation and data modeling are easy to do.

It is very fast to develop solutions.

What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.

The most useful features are the readily available extensions that speed up the work. For instance, KNIME offers multiple document taggers, which one can use with relative ease. Similarly, the number of predefined NER taggers are also very handy.

Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing.

• It has an excellent interface
• Is very easy to use
• It really boosts productivity
• The performance is impressive, even in a normal laptop
• The diversity of tools for pre-processing is very handy
• The integration with R and Weka, make it the perfect Data Mining solution

The most valuable feature is the data wrangling, which is what I mainly use it for.