One of the pros is of course doesn't require license fee. It is also an open source that can connect to Python and R that is capable of customization. Need to mention also the good community support.

It took time to understand the functionalities and familiarize the user interface.

It was the tool I learned the Data Science in the first place. So it is really good and intuitive with its graphical interface. For example you understand train-test split very well because you literally see the split as you work on it. As I progressed and needed more functions and more custom solutions, I started using Python scripts and solved it like that. So it gave me all these abilities.

- Its ease of use makes it possible for non-IT, non-developer, non-CS background people to make data manipulation, preprocessing, mining, visualization and modelling. - It has a graphical interface with nodes and connections so that you don't need to know Python/R to make predictive models or association rules/recommendation systems. - There's a vast library of functions - Even more functions are created by the community so non-existing customized functions are created by the community, via existing functions. - The visual flow of data makes it easy to understand and interpret it. - It teaches the CRISP-DM methodology in an intuitive way thanks to its graphical user interface - It can connect to SQL and similar servers so that the data can be read directly. - It is possible to write own Python/R script for custom needs.

- Custom needs are hard to carry out. - Functions have limited abilities and parameters - Data visualization is weak and relatively primitive - Model development is easy but deployment is hard - It is very slow unfortunately and I think this is KNIME's most important drawback

The two main reasons we used KNIME were to process and prep data, then to conduct machine learning by training models and processing predictions. KNIME is great with data prep and blend as long as the data set is small to medium in size (< 4GB). There were areas where we struggled and that was when models were more complex (> 50 variables) and being able to deploy and schedule jobs. We had to download JDBC drivers for our database connections, which was not something we had to do with other platforms.

There is a wide range of tools to process and prep data in the platform natively and additional tools that can be download within the platform. The ability to customize the settings for most of the tools allows the user to adjust the output. Even more technical settings, like hyperparameter tuning, can be done in the tool UI. There are numerous input and output options and types.

Pulling in very basic files, like Excel spreadsheets can be a bit challenging where other platforms handle files with ease. Also, database connections are not seamless. The Java memory errors also limit the size of data that can be processed without making manual adjustments to settings. Lastly, not being a cloud-based platform, processing big data is very time-consuming.

Good and would recommend to non technical professionals as well

KNIME allowed me to pull data from large google sheets and manipulate them in a clear and easy way. The visual representation of each node makes it really easy to use and understand even for people without a background in data analytics. The KNIME website also provides a lot of resources on using the platform

Very large google sheets containing a lot of data cannot always be extracted due to the size.

I have had a very positive experience with KNIME and like it a lot more than other drag and drop machine learning tools I have tried out.

Some drag and drop tools for machine learning are really limited, but KNIME is not. There are a ton of capabilities of the tool that are built in, and there are even more that are available online, like AutoML. It gives citizen data scientists the ability to create good models without knowing a programming language, and it increases the bandwidth of actual data scientists by allowing them to easily create more models and experiments.

Of course, it is more limited than a programming language, and if you're familiar with building models programmatically, there is a learning curve that will slow you down and limit you at first.

The only tool that we were able to implement for non-technical user adoption. The easy drag and drop flow interface enables our coworkers to develop their own solutions. In terms of functionality it is a real Swiss knife for data processing: Extract information from excel, databases, ERPs, websites. Transform and create any desired output: automatic emails, excels, databases, reports. Python is still available for more advanced functionality.

It is a big memory consuming program. By default tables are stored in memory and to fine-tweak that there's only an .ini file. Nodes that try to auto-guess column types often get it wrong with multiple files, there is a workaround that by creating a more complex workflow.

KNIME is a very good Data Analytics software overall. If the team can handle the slowness issue, it's great for both computer science associates and other employees.

There are multiple features that is great about KNIME. - It has a visual UI that does not require programming knowledge, where you connect nodes by drag and dropping but still do not lose the flexibility of programming languages because it has Python, R and JavaScript nodes too where you can write your own code. - Since it's a visual UI that you work on, it's possible to track down what's going on, similar to an ETL tool. That's something that does not exist on programming. - Community continuously develops Nodes so its like an organism that grows.

Its upsides come with downsides: - Since it's high level (so to speak in CS manner) software. It is far from computer language, and that makes it very slow. It gets even slower more nodes and extensions are installed which was again, an upside. - Nodes have customization and parameter management but they are not as customizable as Python/R libraries, though for that Python/R node can be used.

We were able to build a reproducible workflow for analyzing our data and creating actionable insights.

KNIME desktop is a powerful tool for building analytical workflows. The visual interface is extremely helpful. They also have extensions to integrate other tools like R and Python into the workflows. Best of all you can share your workflows with others - great for reproducible research. There are built in tools for many types of supervised and unsupervised machine learning. The desktop application is free and open source. The support community on the KNIME website is very active and responsive. To extend the features you can purchase KNIME server.

Like any new tool there is a learning curve. However, they have lots of videos, examples and an active support community. There are some features that are not intuitive, such as how to use flow variables. In general I have found that I use R much less now and do most of my analysis in KNIME. KNIME is primary drag and drop and requires little to no coding.

It is a good tool for small business owners, but it lacks the scalability for larger audiences.

It has a well built GUI for visualizing the pipeline for your data-driven applications, and it also comes with a KNIME Server for CRAN job application and deployment of your software

The UI is a little laggy and files can get excessively large, run time and speed is also slow when integrating with other scripting languages.

This is a good software which helps to solve statistical ptoblems,mathematical problems and also algorithm problems.so in Engineering there have lots of problems belongs to aforesaid kind of problems.so this can be useful for pre- Engineers.

This software gives most accurate answers and it has more sensitivity & most of values have precision.

sometimes error messages displaying while works are in progress

Utilizzo KNIME per eseguire analisi multivariate ed identificare relazioni dirette tra variabili. È un software scalabile intuitivo e si è integrato bene con i processi aziendali presenti nel passato. Permette, a me ed ai miei colleghi più junior, si sviluppare algoritmi complessi senza usare codice. di sviluppare

KNIME non richiede skills di programmazione è possibile lavorare con il drag in drop non perdendo la flessibilità del coding in quanto ci sono nodi in cui è possibile scrivere in Python oppure R. Sono presenti add-on per promuovere l’integrazione tra il data mining e l’analisi di serie di dati. KNIME offre un ampia gamma di processi ed il supporto a diversi web reports.

Nel momento in cui i moduli installati diventano molti il lancio del programma richiede minuti. Le integrazioni con altri software sono poche. I messaggi di errore mentre si sta svolgendo il processo lo interrompono.

Utilizzo Knime per analizzare in modo aggregato dati di diversi clienti e produrre analisi benchmark. Nel complesso sono soddisfatta del prodotto vista la sua facilità d’uso e la sua flessibilità.

Il drag & drop rende la piattaforma facile da usare. Mi piace anche il fatto che il sistema sia leggero, veloce e che indichi lo stato di ogni step. Apprezzo la sua estrema flessibilità, che permette di sperimentare.

Le modalità di condivisioni delle analisi presentano parecchie restrizioni, sarebbe necessario aggiungere più opzioni.

Overall KNIME is a solid ETL tool which can automate most of the daily workflows.

The interface is user friendly, the modules are categorized and available for drag and drop on the workflow. Due to this, any complex workflow can be created. Like getting data from a DB, cleansing it, filtering it, blending it with another data source and reporting it is just a breeze in KNIME. Any changes required can be done on a specific module without the need to start from scratch.

In the field of geospatial data analysis, KNIME lags as there are no specialized modules for it, otherwise it gets the work done.

After installing the systems, I just press the "Run" button. A suitable environment to teach machine learning to a beginner.

I like Knime's metanodes the most. I use this feature often. I can add my Python scripts to the stream. Machine learning processes are easy, practical and successful. Knime's performance is fine. Instinctive.

Python betikleri bazen sorunlu olabilir. Knime'da zaman aldığı için Python ile Data Preprocessing yapıyorum.

Knime has turned manual tasks in easy and fast ones just pressing a button to run multiple commands on a excel file for example. Very useful tool to automate and reduce task time.

Knime is very useful to analyze large data quantities with advanced algorithms and code without the need to program because you use block modules that do a specific task and you put program graphically the data processing you want to do connecting this blocks to other in a specific order. You don't need to be a data scientist or engineer to use it is useful in HR and Finance sector to automate manual processes. You can directly input an excel file or database. The software is free but you may purchase a server license to have a process run automatically with large amounts of data from many sources. You can also program if needed and add external blocks to the software.

You actually need more help than provided by block description if you want to do complex data analysys tasks, if you want advanced data analysis and classification you do need to have software engineering knowledge, you can only run a workflow if you have it in the knime folder not any other location of your computer. Knime can take time to initialize if you have many modules installed so install only the ones you need if not it may take 5 minutes or more to open in a regular computer.

We have used Knime to ingest huge volumes of data from multiple data sources. With Knime we cleaned the data and transformed and standardized it. Furthermore, we did a statistical analysis of the data to extract important insights. Workflows were automated to handle data coming every day. All this improved the efficiency of the business processes. We developed reports with the data to convey our consolidated findings for better decision making.

Totally free to use for any purposes Easy and simple UI, easy to get started Excellent community support - Open source with all technical details available No code application, automate and run workflows Can be integrated with external applications like R, Python

Reporting and visualisation functionalities could be improved A large number of features together gives an impression of being cluttered sometimes Memory allocation could be improved

I developed a model and determined its R square using KNIME to explain my model in a stepwise fashion for a presentation.

The graphical interface allows simple drag and drop of objects to process the data. With knowledge of statistics, data science and artificial intelligence, you can implement basic and complex data pipelines. The software is specially useful when you need to explain these models in a stepwise manner to team members who do not possess the technical expertise in programming.

I believe it can sometimes be difficult to debug since it is not documented the same way as python or R studio. That is not to say that the documentation is poor, you will probably find a fix to most, if not all problems that you face through some searching.

great software for modelling any data job - its easy to work with

the UI is a bit clunky - there is scope for making it more modern and easy

I love KNIME and use is often to teach data-science to my students.

It's easy and intuitive. Perfect for beginners to learn the steps and concepts of data-science.

It cannot deal with big data. Every time I tried to analyse a large data, it crashed. So, it makes me feel that it's meant for smaller size of data.

It is like a game, once you have tried two or there times, every tools becomes very simple to use

Sometimes tables are difficult to interpret but the help section provides useful tips

Performed data mining and predictive analytics on this software. Its easy to master and customer support is good.

Doesn't require coding, programming skills to perform data mining.

Visualization capability may not appeal to some.

a) As an ETL tool, It is feasible with the other database and accounting each and every logs. b) It collects, reformat and upload the data sources in a proper structured format. c) The UI is simple to understand even if the person is not familiar with the analytics then also she/he could understand the functions. d) It can be integrated with software like Phython and R softwares.

a) I have tried to integrate knime with the Jupiter notebooks but that was failed.

Knime has given me the speed I need to do my work faster!

I like Knime because you can use no programming language (only the nodes) to retrieve the information you need.

I think Knime doesn't have a platform to learn so well. I struggled a bit at the beginning and the only course I saw from Knime was expensive.

KNIME allows you to focus on the data science and not the programming to get there. The point and click solution is easy to use.

There are many options and in-app support/help/guidance would make this tool even more user-friendly so that the data science team can focus on data tasks.

It is a great program that it'll save you loads of time.

I really enjoy how easy is to transform data from multiple softwares (such as excel, SQL, etc) and manipulate on Knime.

Not user friendly for beginners, there is no clear path for learning the tool. However, as long as you start using it becomes easier.