KNIME Reviews

I use KNIME for analysis-related purposes. I am currently in the process of developing some models for analysis.

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.

The most difficult part of the solution revolves around its areas concerning machine learning and deep learning. The aforementioned area can be considered for improvement.

I have been using KNIME since 2019. I am an end user of the solution.

It is a stable solution.

It is a scalable solution.

I am the only user of the solution in my company. I do provide training to other employees in my company on how to use KNIME.

I have experience with Excel, and I faced some limitations since my company had loads of data to analyze. Considering that my company had loads of data to analyze, I would say I find KNIME to be very useful.

My company has some problems related to the solution's updates. I don't know if there are some restrictions from my organization because of which I cannot update or install some extensions.

The solution can be deployed in a few minutes.

The solution is currently deployed only on my personal computer, which I use in my company.

Only one person or an IT administrator is required to take care of the installation phase of the product.

KNIME is a cheap product. I currently use KNIME's open-source version.

I have experience with Python. Compared to Python, KNIME is better because of the user-friendliness it provides. With KNIME, you don't have to get involved with codes. KNIME provides nodes, making it a very easy tool to use.

I have not received any response from my company, though I had proposed to my organization to buy KNIME so that we can use it on the servers since, right now, it is like a standalone tool used on my personal computer only. I am just a basic and not an advanced user of KNIME. I find KNIME to be a very useful tool.

Speaking about the maintenance phase of the product, I would like to say that I cannot update the solution. If a new version is released, I cannot update the product. I always have to request my organization and the IT team to download and install the product's new version for me.

I recommend others to use KNIME. I have recommended KNIME to my colleagues.

I rate the overall solution an eight out of ten.

I'm a professor at the local university. So, I used it to train virtual students in mechanical engineering.

I'm training a class for mechanical engineers on factory utilization and the basics of data science. That's what I use it for.

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.

In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. 

Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them.

I have been using it for four years. 

I've never had any problems with it, so it's a ten out of ten.

I would rate the scalability a nine out of ten. For a basic training course, it's still fine. But I'm not a professional in using KNIME.

I used RapidMiner. I have not been using it in six years. I used to use it six years ago. Then I switched to KNIME because a lot of my colleagues are using KNIME, so it felt like the right way to do it.  

Moreover, I switched from one university to another, and at my new university, other colleagues are using KNIME as well. So, for the students, it's easier to go just with one product.

Overall, it's still easier than using Python, so it's still fine. But, actually, they made it more complex by switching from the last version to the one before.

We're using the free academic license just locally. I went for KNIME because they have a free academic license. And to be honest, I never bothered to check the prices.

I like it a lot. I would advise that you shouldn't be afraid of data science. It's actually straightforward.

Overall, I would rate the solution a nine out of ten. 

As a university professor instructing courses on data mining and machine learning, I incorporate both KNIME and another software application into my teaching. This approach allows me to demonstrate various use cases effectively. I actively engage my students by having them utilize both software applications, providing practical hands-on experience in the areas of data mining and machine learning.

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

To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages.

I have been using it for more than ten years.

I would rate its stability capabilities nine out of ten.

It provides good scalability abilities, I would rate it eight out of ten. Currently, more than sixty individuals use it on a daily basis.

They are helpful and I am highly satisfied with their customer support services. I would rate it nine out of ten.

Positive

We use Orange as well.

The initial setup is straightforward.

While there are certain limitations in functionality, you can still utilize it efficiently free of charge.

I would recommend it, especially for those who prefer not to program or have limited coding intervention. Overall, I would rate it nine out of ten.

I'm a professor, and I learned about KNIME from a data science course. I use KNIME for data visualization, manipulation, and generation.

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.

Though I can use KNIME in a 64-bit platform in the lab, it's missing some features. For example, from my laptop, I can use the image reader feature of KNIME. However, in the lab, the image reader node is missing. This is an area for improvement because not all nodes appear in the 64-bit system. In other systems, you get to use all nodes or features. It should be uniform in all systems, though I have no idea if it's a software problem or a corruption in the system that's in the college lab. At home, I can see and access the image reader node, but in school, that node is missing.

What I want to be added to KNIME is the feature of extracting data from social media platforms, whether structured or unstructured and for that data to be automatically converted into a CSV file that I can use. I want a data cleaning and collection process from KNIME that works for social media platforms because datasets in social media can serve as business intelligence or can aid businesses. Social media is the trend nowadays, so I want a KNIME node to analyze data from social media platforms.

I'd been using KNIME for three months now.

In my three months of using KNIME, its stability has been okay, but I want to explore the tool more. Stability-wise, it's a seven out of ten. When the network is stable, then KNIME is okay to use.

KNIME is a scalable product, so I'm rating it as nine in terms of scalability.

I've not used any other tool besides KNIME, but I'm planning to try RapidMiner, which I've not yet installed.

KNIME was easy to set up, so it's a ten out of ten, setup-wise.

KNIME is an open-source tool, so it's free to use.

At the moment, only ten students use KNIME. The course just started.

In my three months of experience with KNIME, it's an eight out of ten.

We use KNIME for two main reasons:

Automation: The main purpose of our utilization it to run scheduled workflows (such as CRM campaigns, business reports, etc.) on a recurrent basis. We have created ETLs to automate most of the recurrent tasks and we let it run via batch files.

Ad-hoc business cases: We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders.

In addition to leveraging KNIME flexibility to query data from our database for ad-hoc business cases, the product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine. In some particular cases, my team builds the workflow and then we hand it over to the stakeholder, who can run it on his own by inserting a variable or changing a few settings in the workflows.

• 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 think the data visualization part is the area most in need of improvement.

I’ve been using KNIME for more than four years now. I started using it in the company I was working for in Rome (Paddy Power Italy), then I moved to headquarters in Dublin (Paddy Power Ireland/UK) and started using it for their business. Eventually, I moved to the United States and started using it for my current company (TVG-Betfair) and it is currently the main analytics tool in both our offices (New Jersey and Los Angeles).

I would definitely rate it a nine out of 10. I am very happy with the product and it would be hard to find something better in the market.

We use the solution for data analytics and logic design.

The product is working fine with Oracle.

It is is written in Java. If they can output the Javascript, it will be much better. Also, it could be integrated with Visual Studio.

I have been using KNIME for three years.

20 users are using this solution. Scalability is quite easy, but handling many notes can become messy.

Most of the things is available in the community.

It's quite easy to setup.

I have a CSV reader. When I reset that CS reader, and It gave some error.

I have a CSV reader, and I encounter an error whenever I try to save. However, if I reset the CSV reader, I am able to save successfully. It’s a rare issue, but there's something wrong with the CSV reader. The error message doesn't provide a solution, only indicating a problem with the CSV reader.

I want to save the project but always face saving issues. If I reset the node, the saving works fine. The error message isn’t clear about what is wrong or how to fix it. I discovered on my own that resetting the CSV reader from green to yellow allows me to save the project. This issue is quite rare.

Last Friday, there was a widespread CrowdStrike issue, and I had to restart my computer. After restarting, I lost my entire project.

I recommend the solution.

Overall, I rate the solution a nine out of ten.

It's mostly data preprocessing, handling, and processing (ETL) processes, as well as expanding the transport load. 

Additionally, we also work on various machine learning tasks, such as regression models and other small topics related to machine learning.

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.

There are a few aspects that I am not entirely satisfied with. For instance, when integrating KNIME with our SAP system ERP and HANA, it's not as straightforward as expected. We need to find alternative connectors like the Teradata connector, which adds complexity.

So far, I've had some problems integrating KNIME with other solutions. Thus, it could be an area of improvement. 

We have been using KNIME for two years.

Overall, the product has been stable. It has efficiently handled the tasks we have encountered so far.

There are two end-users using KNIME in our organization. Because we are still beginners, we are only using it to learn how it works and get a better understanding of the system. We are not yet certain if we will use it extensively for all topics.

The initial setup was easy. 

I deployed the solution myself. 

We use the free version only. 

We are working with KNIME on some small projects, but we are also looking for an alternative solution to explore.

Overall, I would rate KNIME a seven out of ten because we faced a problem with the integration with other products, like SAP.

This solution is primarily used for various data analytics in an enterprise environment. 

The reality of any data analytics project including Data Science is that 90% of the effort goes into data sourcing and preparation. Data usually comes from multiple sources including data warehouses, web scraping, Excel input, free text, etc. KNIME allows you to do the 90% plus other predictive functionality.

It is a free open-source tool that performs very similarly to other expensive tools. KNIME has been great for me over the years. It allows me to connect to various sources including data warehouses, then put the processing logic together (ETL-like), which can be quite complex and produce the required output. Ultimately, it would go into Excel or Tableau for presentation.

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.

The areas that I feel need improvement are:

• It needs support for a joiner node to have three outputs (left unmatched, matched, right unmatched), as competitors do (have not checked 2019/20 releases).
• I need the ability to add additional comparison conditions to a join. For example, in SQL you can specify only rows with a date fitting within a date range from the joined file. At the moment in KNIME, you should allow a join explosion to take place and filter what you need later, but sometimes the output becomes too big.
• It would be helpful to have more examples of Java code for nodes, like Java Snippet.
• I would like to have this solution show row counts on canvas, as it would improve the control and speed to build the workflow.
• The pseudo-code types could be rationalised into one (e.g. only Java).
• I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script.

I have been using KNIME for between four and five years.

My system occasionally may crash like other similar tools, although autosave is available.

Scalability is limited to a desktop application.

Obviously, as an open-source application, your options are limited but I have found answers on forums when I needed help.

Recently I have been using Alteryx so I have collected a few points on differences in both tools. Both are good, I can conclusively say I could go back to KNIME and be as effective data professional as I am with Alteryx.

I have to use Alteryx due to my client's tool choice, but I know that what I am doing with Alteryx right now could be done better in KNIME. Of course, Alteryx has its own advantages for certain areas.

It is a relatively simple install. You can even avoid installing it and run from a directory.

KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website. The fact that KNIME is open source may create challenges from an IT security view in an enterprise environment.

For this review, I would include Alteryx and Lavastorm (the latter is no longer available).

If you need a good Visualisation functionality, you should use Tableau or something of that caliber. However, the data prep can be done KNIME, which would give you extra confidence that what goes into your Visualisation layer is correct.

Overall, KNIME is definitely worth considering.