KNIME Business Hub Reviews

I am currently using KNIME Business Hub. In my experience, using KNIME Business Hub as a unified platform for developing advanced analytics and artificial intelligence solutions enables distributed processing of large-scale data through Spark. Implementation of modern lakehouse architectures that integrate data engineering, data science, and analytics within a single environment enhances scalability, model versioning, and team collaboration. Currently, I use KNIME Business Hub to build data pipelines, train models, and deploy analytical solutions into production environments.

I am also using other tools because my company has many clients and our clients have different tools. We need to construct the analytical solutions in these tools. For example, I am using Python because in Python we construct the statistical and analytical models. Python is the primary language for developing advanced analytics and artificial intelligence solutions, including machine learning, deep learning, and large-scale data processing. My company has strong experience with different libraries, such as Pandas, NumPy, Scikit-learn, and TensorFlow. For our clients, we need to build, validate, and optimize predictive models. My team is multidisciplinary, and we integrate solutions into production environments through APIs, process automation, and end-to-end analytical pipelines, ensuring scalability and maintainability of the models. I always use Python as well. However, I use KNIME Business Hub in the same way because KNIME Business Hub is very important for constructing advanced analytical models. KNIME Business Hub now has many nodes to use for big data, data quality, data governance, and advanced analytics. We use KNIME Business Hub as well. It depends on the client because we always try to analyze what tool our client has, and then we try to use this tool. KNIME Business Hub is another tool that we now use, and we use the Python nodes as well for advanced analytics. In data governance, we try to use KNIME Business Hub to construct the data quality rules and other analysis. For example, to assess and understand the maturity of the companies, we sometimes use KNIME Business Hub. I use different tools, but sometimes KNIME Business Hub, and other times Python and KNIME Business Hub are different tools. I also use Amazon Web Service and Azure.

My experience using KNIME Business Hub for the development of advanced analytics and machine learning solutions leverages a wide range of nodes across data preparation, modeling, and deployment stages. I always try to use specific nodes because we always try to use the CRISP-DM methodology, so we need to always do data preparation and transformation for advanced analytics solutions. Key nodes and components used include data preparation and transformation nodes such as File Reader, Row Filter, Column Filter, Missing Value, String Manipulation, Math Formula, Joiner, GroupBy, Pivoting, and Rule Engine. I use nodes for feature engineering, such as Normalizer, One to Many, Binner, Lag Column, and Feature Selection Loop, and other nodes for machine learning and AI. For example, Partitioning, Decision Tree Learner, Predictor, and Random Forest Learner are all models that KNIME Business Hub has, and we use them for our models. Sometimes, I always try to use the Python and R nodes because there I can program the code as well. For model evaluation, I use other nodes, such as Scorer, Confusion Matrix, and Numeric Scorer. I love KNIME Business Hub because I can construct workflow automation and deployment. For me, it is very clear to understand the process for constructing analytical and advanced statistical models. It is good for me to use KNIME Business Hub for that. I use KNIME Business Hub end-to-end, from data preparation and feature engineering to machine learning, model evaluation, and workflow automation, integrating Python and R when more advanced modeling is required. I always try to use KNIME Business Hub.

It is very important that I have the workflow automation integrated with Python nodes, for example, and I can construct our main code to construct the solutions. For us, it is very important to have the workflow automation. In KNIME Business Hub, it is possible because we have the end-to-end approach to the models. We have, for example, some nodes for data preparation, and other nodes for feature engineering, and other nodes for machine learning and model evaluation, for example. We have only one workflow with all the nodes and all the processes. For us, this is an important impact because, for example, we have to construct segmentation models for our customers, and we define a frequency to run the models. For example, we need to run the cluster segmentation around each month. We have the automation of the workflow and we need only to put a run in a button and the process runs. For us, this is an important impact because the time to obtain the results is very quick.

Sometimes it is a little bit difficult to use some nodes when we have many large-scale data, for example, CSV files with a large amount of data. It is sometimes difficult to try to import the data in KNIME Business Hub nodes because I think that some features that are in the CSV in text, for example, large text, is difficult for KNIME Business Hub to import these fields. I don't know why, but it is very difficult. We need to try to use different nodes for importing the data, such as File Reader and CSV Reader. However, I think that it is always the features that have much text, it is difficult for KNIME Business Hub to understand and import this information. I don't know why, or maybe I don't know if we don't know what the better option is to configure the node to import all the CSV or the data set. However, we have always had this problem. In some nodes, sometimes it is the same because sometimes, for example, I have a CSV and in my CSV, I have a feature that is, for example, a date. When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text, for example. We try to use their nodes that convert text to date, but sometimes it is difficult, and it is not immediate to transform the text into a date. So we needed to convert the text into a date in the CSV, and then import it again in the KNIME Business Hub node and try to have a good read of this field. I know that KNIME Business Hub has some nodes to convert text to date and others, but sometimes it is difficult to use these nodes. I don't know why. Maybe it needs a specific format for the date and we need to transform our feature in this option. So sometimes it is a large process to convert these features. However, sometimes we need to investigate and search for other nodes, and try with other nodes to import these cases.

I started with KNIME Business Hub around fifteen years ago.

For me, it is great. I think that sometimes we have some missing problems in some nodes when we are constructing the statistical models, but we always try to visit the forum for KNIME Business Hub and then we try to resolve the problem. However, I think that for now, I need to come back again to Germany to make another training because I saw that KNIME Business Hub now has many new nodes and I need to explore the new nodes and try to use more. For now, KNIME Business Hub is excellent for me and for our team.

We are a partner from KNIME Business Hub at this moment and I made different certifications in Germany, in Berlin, with KNIME Business Hub about machine learning nodes. I think that was around 2016. In 2018, we made two certifications with KNIME Business Hub.

For now, we always try to use KNIME Business Hub to integrate with Power BI because we use Power BI to present the results and the visualization for the models. In KNIME Business Hub, I try to use some graphics, but for our internal analysis. For our clients, we use Power BI to present the results for the models.

I think that KNIME Business Hub is very robust and is a leading solution for analytics and advanced analytics. I think that now we have many nodes to construct the analytical models in the big data nodes and to process structured data. This is important because it is very easy to use the nodes in KNIME Business Hub in these cases. For example, in Python, it is a little bit complex to construct the code. In KNIME Business Hub, we have the end-to-end approach to the workflow, the complete workflow to resolve the process for the model. This is very good to have good results and quick results for advanced solutions, for analytics and for artificial intelligence. I think that I prefer KNIME Business Hub to Python, for example.

I think that the price is good. I think that a good option is to analyze, for example, the cost for Amazon Web Service, AI components of Azure and Amazon, and try to compare to KNIME Business Hub, and I think that it is a good price. However, always in our solutions, we need to make a good calculation for all the solutions because we have many solutions, and because all our clients don't have KNIME Business Hub. Sometimes we use KNIME Business Hub for our internal development of the analytical models. However, sometimes our clients have KNIME Business Hub, so it is perfect because we can construct the models there. When our clients don't have KNIME Business Hub, we need to use other tools because sometimes our clients tell us that they need us to construct the model only in their tool, for example, Amazon Web Service or in Python, so we need to construct there. Because sometimes they don't know about KNIME Business Hub and they want to use the tools that they have. However, I think that it is comfortable to use KNIME Business Hub for our clients. They like it very much because it is very easy and now it is very robust for statistical and advanced analytical solutions. My overall rating for KNIME Business Hub is eight out of ten.

I mainly use KNIME Business Hub currently for data ETLs and then it meets with predictive analytics. Sometimes I utilize it for forecasting, but mostly it's predictive analytics.

I have utilized both model building relevance nodes in KNIME Business Hub, and there is the capability of deploying the model on the virtual PC so that everybody can utilize the model and send a query. Essentially, without an expensive decision engine, you can just deploy your model and utilize it, whether it's in an app, in something else, or on a website, it doesn't matter. You can just ask a question and the predictive model runs and you can get the results.

Collection of company-wide information is one of the main benefits that KNIME Business Hub provides to the end users; all the intellectual property that has been developed in a central location is critical. When somebody leaves the company and another one comes in, you can have all the information on KNIME Business Hub about what's working so far, what's deployed, and other relevant details. That actually provides a lot of information, so instead of doing something on your own computer, it's all centralized. It actually increases the general maturity of the company, including not only the user but the general maturity of the company and the line of businesses. All of the workflows are stored; I know who utilizes it, who built it, when, and how frequently it's used. A lot of insights are important, but in general, I have a lot of intellectual property built-in so I can keep track of it. It's very similar to GitLab, but this is for analytics and for the company itself.

On the computer vision part within KNIME Business Hub, it's not that capable, unfortunately. I utilize some of the existing nodes, but other than that, if I am handling classic tabular data, it's fine. If I'm handling unstructured data sets, I can push it to large language models and get some results back, and that's also fine. However, when it comes to visual analysis or vocal analysis, that lags a bit.

Integration capabilities of KNIME are almost simple. I have integrated with enterprise resource planning systems, SAP, and all that. With SAP it is working well, but it could have been better.

I would describe KNIME Decision Hub as somewhat helpful in making data-driven decisions more efficient. It could have been a scalable decisioning as a service at the back end, but it's not working that way. I can deploy a model and get some results by sending some queries, but if I happen to utilize it for a more scaled-up business such as a bank or telco, that is a different situation. Then, I start requiring hundreds, thousands of dollars worth of licensed software, and I wish it would be capable of scaling up and down as needed.

Visual analytics is the main point for improvement for KNIME Business Hub. Computer vision is the most important because now there is a new age of large language models and visual language models. The visual language models can turn an image or a video into text, and you can utilize it as if it's a very capable computer vision model. It tracks all the segments and all the labels on the images or videos, which means that if I can interact with these through KNIME Business Hub, then I can build very sophisticated analytics end-to-end. Right now, that's not that much possible, but I wish it's going to be in the near future.

If we talk about functionality, I would like to see most of the classic independent large language models and visual language models integrated into the next version of KNIME Business Hub. There are new multimodal capabilities in it, and I have to go grab a model from the open-weights structure, implement it somewhere, and send some queries. I wish that most of that would have been built-in. Databricks, for example, built models embedded into their software structure so that I don't need to go to a third-party; I can run some models to enrich my data. This includes enriching images into tabular data sets or converting voice into tabular data sets. Many enterprises cannot just push the existing data outbound and get some results back since a lot of that consists of internal data sets.

I have been working with KNIME Business Hub since 2021.

Most of the cases that I utilize from KNIME Business Hub require data from databases with semi-structured, almost clean data sets. However, more and more required data starts coming from Internet of Things devices, and these are streaming data sets, not static data sets living in databases. I need event listeners that listen to something, on-the-fly score it, put it into context, and send it to a large language model. Complex event processing is becoming much more of a requirement, and in that case, I need milliseconds of performance. KNIME Business Hub lags there a little bit, but it's improving.

First, I deployed KNIME Business Hub on-premises, such as on my machines, because it is low-weight. I can immediately install it onto my personal computer or even my tablet, run some data on it, and get some insights immediately. I don't need a huge footprint or an expensive hardware-plus-software combination. That's a good aspect. But at some point, if I want to excel in a company with one hundred users or fifty users, it has to be on either my own servers or a private cloud.

KNIME Business Hub can also be deployed on Amazon Web Services, Azure, or Google Cloud Platform based on the client's requirements. Amazon Web Services is the best option, but I haven't seen anything I can purchase from Amazon Web Services's marketplace. That would be a good benefit for any client to onboard immediately. Just click it down from the marketplace, and if I have corporate credits, I could utilize it there so that suddenly everybody at a bank, for example, owns a KNIME Business Hub license. That would be awesome.

Pricing for KNIME Business Hub is much cheaper than the competitors. It's a reasonable amount of money for the product.

I am already advising many companies to start machine learning and artificial intelligence integration with KNIME Business Hub because a lot of the large language models need to be integrated with classic basic machine learning to turn it into something called composable artificial intelligence. I generate a lot of data from text and video, push it into a machine learning model, push it to a forecast, get the results, and then export it with a large language model, talking as a normal person would. This is the composability part of it. If you want to do composable artificial intelligence and get return on investment fast, go with KNIME Business Hub. Don't go to software as a service, Databricks, or IBM Watson. These tools are both expensive and very capable but will take months to build something and get positive return on investment, leading to months in negative return on investment. With KNIME Business Hub, you can immediately grab some data and see positive return on investment. They have single-user free licenses, so I can start now on my Mac, throw some results, and do a proof of concept or proof of value for upper management in just a few weeks. Then I purchase KNIME Business Hub, and the company begins benefiting, leading to immediate positive return on investment. That is the difference that many don't understand. I would rate this product nine out of ten.

My use case for KNIME Business Hub includes automation, querying from the database, and outputting to Excel and creating charts.

In my opinion, the most useful functions or features in KNIME Business Hub are its easy-to-use database query configuration, which requires no programming, although I use some Python as well. However, I sometimes prefer not to use Python because it is easy to use KNIME Business Hub.

I believe the main benefits I receive from KNIME Business Hub are automation. When I work through the workflow one time, I can reuse it later on, saving considerable time for many tasks. I only need to work through the workflow once and then I can reuse it quite easily by configuring some parameters in the node. The automation is quite helpful.

Regarding integration capabilities, I do not think it is that easy to integrate KNIME Business Hub with another product because the connector does not have many options. For example, if I want to connect to some OpenAI API, I still cannot find solutions for that.

I do not use the collaboration features within KNIME Business Hub, and I think the trend for data is more oriented toward generative AI. I believe KNIME Business Hub needs to catch up with this trend. If you want users to use generative AI, then you need to provide the features for generative AI.

In my opinion, KNIME Business Hub can make improvements by having more integration with Python or JavaScript, especially to work with generative AI better.

I would like to see additional functions in KNIME Business Hub that can connect to generative AI, allowing users to describe the workflow for easier workflow generation and creation. Normally, not all people know which nodes in KNIME Business Hub can be used, so if you describe the workflow, it would help to draft at least the nodes, making it more helpful.

I have been working with KNIME Business Hub for five years.

From 1 to 10, I would rate the stability of KNIME Business Hub quite good, around an 8 or 9.

In terms of scalability, I think KNIME Business Hub is around an 8.

My mark for technical support for KNIME Business Hub is about a 7, as most of the support is in the community, and it is quite good because it is open source. Most people go through the community to get help, and it is quite easy to get examples as well.

For the setup of KNIME Business Hub, I think it is quite easy because you do not need to have many things to set up. However, sometimes I find there is something wrong. For example, even though I save and then come back, it says something is wrong, and I have no idea why such things happen. Whenever I save something and then come back, it is just that some of the nodes have been deleted, which is very rare.

I am using some analytics tools in the product, and for analytics, it is easy to do the data manipulation and easy to do the data blending. I have not used data visualization very much in KNIME Business Hub. My overall review rating for KNIME Business Hub is 9.

I use KNIME for a wide variety of purposes. Most recently, I employed it in a survey and experiment to explore different teaching methods, specifically using software to help nurses learn drug dose calculations. This became particularly important during the pandemic when there was an urgent need to reskill healthcare professionals rapidly.

During the early days of the pandemic, no one knew how to treat COVID-19, and it was a dire situation with high mortality rates. The healthcare system had to adapt quickly, with retired nurses and doctors being called back into service and nursing students stepping up to meet the demand. A program I had previously worked on, which taught drug dose calculations, became crucial again. Nurses had to familiarize themselves with a whole new set of drugs.

I analyzed the data from this program using KNIME. I handled various statistical analyses, including mean, standard deviations, regression, correlation, and Wilcoxon signed-rank tests.

It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data.

Next, you can clean the data by handling missing values, selecting specific columns you want to analyze, and then proceeding with your analysis, such as regression or correlation. KNIME has over 4,500 nodes available for download. 

In addition, KNIME offers various extensions. For instance, the text processing extension allows you to process text data efficiently. It's more powerful than other tools like NVivo and Palantir.

KNIME also has AI capabilities. If you're unsure about the next step, the AI assistant can suggest the most frequently used nodes based on your previous work.

Another valuable feature is the integration with Python. If you need to perform a task that requires Python, you can simply add a Python node, write the necessary code,

The hardest part is keeping a tidy workspace because of the many nodes involved. When teaching, it would be helpful if there was more emphasis on how to group nodes effectively. For example, turning frequently used nodes into a single component can simplify things.

I have been using the product for three to four years. 

When I had to do things new to me and didn't know how to proceed, I used the KNIME hub and community forum. Both times, I received answers within twelve hours that solved my problems. From that experience, I'd say their support is superb, even on the free version, and I imagine it's even better on the paid version.

I use the solution's free version. 

The tool has a community, and people are very helpful. I've been impressed by that. I'm trying to think of ways I want to improve. I did start it up on a Windows PC. It seemed a bit easier for some reason, but I'm now on a Mac. I have trouble with the magic mouse, but that's an issue with any software not specific to KNIME. It worked well when I received data from other people. I haven't tested it on a ten-billion-item database, but it works well for hundreds of thousands to a million items. I haven't tried the big databases because that's for the commercial version. 

The solution has a lot of video material for teaching and upskilling people. If I've got a colleague that I just introduced to it, I send them links to two videos, and they learn from that. That's two hours of work for them in two sessions, and they've got a programming language under their belt. 

For instance, I've never used it to build a neural net, though it has many different neural nets. I have used it for decision trees, regression models, and so on, and KNIME has been very easy to use for those. It takes much of the work away from you. For example, if I build a decision tree, I usually want to take several samples from your training data and then choose the best outcome.

With the tool, you just put the Decision Tree Learner node in there, set what percentage you want for training and testing, say 60% for learning and 40% for testing, and how many times you want to do it, like seven times. It runs seven samples, does the training and testing, and reports back to produce a model. Then, you can use a Scorer node to score your results. I produced the first-ever machine learning exercise in nursing in 2008, which wasn't using KNIME, but I think KNIME was available then. That project was associated with a reduction in the dropout rate from the university from 19% to five percent over three years. And you can do all that for free with KNIME, even with a dataset of about 1,200 students, on your PC with no trouble.

I read a report from Uppsala in Sweden where, during the pandemic, they had to repurpose many drugs developed for other diseases to find out which ones would be useful for COVID. They went through thousands and millions of drugs, and it seemed to reduce the time to develop or choose drugs by a major factor.

The solution accessed about eight or ten different chemical databases, biobanks, and similar resources and could integrate with all of them. One of the good things about KNIME is its ability to read different data sources. For instance, it has platform connectors, allowing you to link to various external systems. It's just a matter of plugging in a node and linking it up without usually needing to write new code. For example, in my use case, reading from an Excel file used to require configuring a node manually, but now you choose the file you want to analyze, put it on your workspace, and it recognizes the file structure automatically, whether it's an Excel file, CSV, or tab-delimited file, and provides the correct node. It's quite intelligent.

If you’re considering using the solution for the first time, I recommend starting with a one-hour introductory video to get a basic understanding. If you need more information, check out KNIME TV. They have a channel with hundreds of short videos, usually five to 30 minutes long. These videos show you how to drag and drop nodes, configure them, and run them to see the results. It’s a very visual way of learning.

I rate the overall product a nine out of ten. 

I primarily use KNIME for ETL, extracting data from different sources. I extract data from endpoints of Drupal created for me by developers, then transfer this data into Oracle. After extracting, I create a model in Oracle with ETL, which is used by Power BI. Following this, I create a star schema of the data.

KNIME is simple and allows for fast project development due to its reusability. I appreciate the ability to make improvements or modifications in existing workflows. Although I have not yet used the forecasting and customer profiling features, I find them promising. 

Another effective feature is the ability to use GET request objects to retrieve data from websites or APIs. This makes iterative steps easy to manage. It is more elastic and modern compared to SAP Data Services, allowing node creation and regrouping components or steps for reuse in different projects.

I have seen the potential to interact with Python, which is currently a bit limited. I am interested in the newer version, 5.4, when it becomes available. The machine learning and profileration aspects are fascinating and align with my academic background in statistics.

I have been working with KNIME for almost five years now.

Occasionally, when using the GET object, there might be issues due to the velocity of the lines or the IT system of the commission. Overall, stability is not a significant concern.

I have not encountered any scalability limitations with KNIME at the moment.

I contacted their technical support around five times. While they cannot always provide immediate answers, they are generally efficient and simplify tasks, especially in the initial phase of learning KNIME.

Neutral

I also worked with Power BI and BusinessObjects and have experience with typical data services access.

The initial setup was straightforward, taking between half an hour and an hour depending on the data entity.

I use SAP Data Services as well, but I find KNIME more elastic and modern.

I am impressed by the modularity and reusability in KNIME, especially the ability to make small adjustments to object configurations. I am interested in its interaction with Python and machine learning aspects. Also, I recommend KNIME to others as I face difficulty finding reasons not to. My overall rating for KNIME is between nine and ten.

Rather than specific use cases, I've used it in different sectors. Mostly education. For example, we've used it to predict the kinds of courses or degrees students should pursue based on their skill set and learning capability.

KNIME is very easy to handle and use. Anyone can use it, and it's easy to learn. You don't need a specific class. They're very good at model prediction. It has got everything.

From data analysis to model prediction and application, it's very good. I only use the free community edition, not the enterprise one. I feel KNIME is really good. I haven't tried any other tool or platform yet, but KNIME is pretty good.

The workflow is great. You drag and drop, and then you have the data explorer and charts that give results. 

The execution is also good – it's easy to identify where your model has gone wrong. It shows you the exact point of error within the workflow, so you don't have to execute the entire workflow to find it. For example, if your workflow has ten steps and the error is in the sixth step, it will show you the error at that step. You don't have to worry about the first five steps.

The Data Explorer is very good, and the charts are great too. The accuracy charts for different models, like decision tree, K3, Naive Bayes, are all very good. KNIME is great at reporting, whether it's structured or unstructured data. These are all very good features.

One disadvantage is the data load. KNIME has limitations. It doesn't handle large datasets or a high number of records well. I haven't tried more than 10,000 to 20,000 records because the model prediction doesn't come out well with more data. The Enterprise Edition might work better, but I've only used the Community Edition.

That's the only disadvantage I've encountered so far.  

I used it for four to five months, from the beginning of last year until January. 

It is stable. I would rate the stability a nine out of ten.

Scalability is also very good. Both the older and newer versions have improved. I would rate the scalability an eight out of ten.

I haven't needed it yet. I've been able to do everything myself.

I would rate my experience with the initial setup an eight out of ten, with ten being easy. 

I use the open-source version. I haven't explored the paid options yet.

Overall, I would rate it a five out of ten.

It is suitable for various industries, including government, enterprise business, product manufacturing, and banking and finance. KNIME provides effective solutions for data-related tasks.

It takes and improves performance analytics.

It offers a node-based data integration and processing system connected through a user-friendly drag-and-drop interface. This makes it an excellent choice for data analytics and engineering tasks. We focus on integrating important features with a strong emphasis on security.

The current UI is primarily in English. Analyzing data in local languages might present challenges or issues.

I have been using KNIME as a partner for three years.

I rate the solution’s stability an eight out of ten.

It lacks third-party connections that influence scalability. 3 people are using this solution. It is suitable for small to medium.

I rate the solution’s scalability a six out of ten.

KNIME offers online training, which is quite effective. The training includes various modules, and completing them can lead to certification.

Positive

The server versions have no designated hardware requirements for initial installation and configuration. This can lead to difficulties, such as errors related to hardware and sizing. Providing the right hardware and understanding the setup requirements is crucial for proper configuration.

I rate the initial setup an eight out of ten, where one is difficult and ten is easy.

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

KNIME offers a free starter tool with a desktop version that allows you to manage the entire data lifecycle and build data pipelines. This is suitable for small data science or engineering teams. However, if you need to deploy KNIME at an enterprise level or require advanced features, you must purchase a license. The pricing is reasonable compared to alternative tools, but it might be beneficial if there were more affordable options for small businesses or SMEs.

Overall, I rate the solution an eight out of ten.

KNIME is an analytics platform. We use the tool for prototyping solutions by using flat files without database connections. We use it for the decision tree models.

The product is user-friendly. It is easy to use. My experience is very good. We could see the benefits of the solution immediately after deployment. It is a complete solution. The documentation is very good.

The support could be improved. We do not have much documentation in Portuguese.

I have been using the solution for two years.

The product’s stability is very good.

The scalability is good, but we do not have the production environment to put the models on the cloud.

The deployment is easy. The deployment takes 15 days.

We did the deployment in-house. We needed six people for the deployment. We do not have to maintain the tool.

KNIME Business Hub is expensive for small companies. The desktop version of the tool is affordable. The analytics platform is free.

We usually use machine learning tools to push the models into production. The desktop version does not have the feature to scan models. We must buy KNIME Business Hub if we need those features. It is more expensive than machine learning tools.

KNIME’s desktop version is more suitable for prototyping than machine learning tools. New users must go through the documentation to understand the components. It's a user-friendly tool. If we have machine learning skills, we can use the tool easily. Overall, I rate the solution a nine out of ten.

KNIME is an excellent product, and I've used many other platforms like Google Collab, Azure, and even AWS. However, KNIME, especially for AI and machine learning, is very different. It's almost no-code. You can add code if needed, but it's not necessary.

KNIME has hundreds, maybe even thousands of modules, which are called nodes. These nodes, along with their libraries, are essential for solving specific issues or problems. You can select the nodes you need, and they come pre-recorded as visual boxes. You just need to assemble the nodes required for your solution. As mentioned earlier, you can search for libraries and select the appropriate nodes, then combine them to form your entire workflow. KNIME supports coding in Python and other languages, but you can assemble the nodes visually without writing code. Each node has a specific function, and if one node doesn't suit your needs, you can easily replace it with a different one.

Additionally, each node has inputs and outputs, and you can configure them based on your requirements. Once the nodes are set up, you can attach the data and let it flow through the nodes to execute your workflow.

One significant improvement is its speed. With KNIME, you can accomplish many tasks in a single day. It's very fast since you mostly work with prebuilt nodes and libraries. Also, the latest version allows us to add Python code if needed.

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.

One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well. 

However, if you encounter very complex requirements, you might need to add custom code to achieve your desired outcomes. This is an area that could use some improvement, but the advantage is that it encourages you to evaluate and minimize coding efforts. As a result, you can reduce the overall amount of coding required, which is a positive aspect of KNIME.

Another area that could be improved is related to the libraries. While they are quite extensive, they might not always match your exact needs. In such cases, you might have to do some coding to tailor the solution accordingly.

Therefore, one area for improvement is the flexibility of prebuilt nodes, as they may not always match complex needs perfectly. Also, enhancing clarity on what the nodes do would be beneficial.

For additional features, there are a couple of things that come to mind. Firstly, it would be great to have more clarity on what each node does. Sometimes, it's not very apparent, and additional information would be helpful. 

Secondly, it would be beneficial to have better ways to interact with and manage nodes, enhancing the user experience. 

And finally, I think KNIME could improve on how easily it allows for extending functionalities with custom code. Although it's relatively straightforward now, making it even more accessible would be advantageous.

We have been using KNIME for two years. We currently use the latest version.

Stability is excellent. I would give it a nine out of ten.

As for the on-prem version, I would rate the scalability around a seven out of ten because it's definitely scalable, but we haven't really pushed it to its limits.

KNIME provides good support. The only challenge is that they are in Germany, so sometimes the time difference can be a factor. As it's a free product, they may not be available all the time. But the platform itself is easy to use, and they have very good documentation, so we rarely need technical support.

Neutral

The deployment is not very hard or time-consuming on-premises. The only challenge is dealing with hardware limitations like memory and GPUs.

Currently, we deploy KNIME on-premises, but there is a paid cloud option available.

We have seen an ROI. In my case, as a consultant, I can create proofs of concept very quickly using KNIME. For example, if a client wants to explore a specific idea but is already committed to using platforms like Azure, Google Analytics, or AWS, we can still use KNIME to demonstrate the concept. This allows us to try out new ideas and algorithms before implementing the full project on their chosen platform, such as AWS, if needed.

The proof of concept approach is especially helpful when clients need to validate the feasibility of certain algorithms or machine learning techniques. 

The price for the cloud version is very reasonable compared to other products at the same scale. If you expand to the same scale, KNIME could be a more cost-effective option.

If you're evaluating KNIME, make sure to use a comprehensive use case. Sometimes, users might not find the nodes they need in the libraries, but most likely, it's due to improper searching. KNIME offers a unique platform with a wide range of nodes, so thorough exploration is essential to fully benefit from its capabilities.

Overall, I would rate the solution a seven out of ten because I have not yet tried every feature. Otherwise, KNIME is really a great product.

We use KNIME for analyzing data, for ETLs, and analyzing for machine learning.

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.

The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data.

I have been using KNIME for eight years.

KNIME is a stable solution. In the previous version, sometimes KNIME would get stuck, and we had to restart the server too many times. Sometimes, we faced a lack of memory issues with the solution.

I rate KNIME an eight out of ten for stability.

Less than ten users are using KNIME in our organization.

I rate KNIME an eight out of ten for scalability.

KNIME’s technical support team responds quickly. You can write your problems in the solution's forum, and they will answer you.

KNIME's initial setup is not easy and needs someone who knows Linux to do it.

A Linux engineer can deploy KNIME quickly, whereas someone who doesn't know Linux will take longer.

There is no cost for using KNIME because it is an open-source solution, but you have to pay if you need a server.

KNIME is a perfect solution for small and big companies, especially people who are using Excel. KNIME is very easy to learn and implement, and doctors and lab personnel can use it. Lots of companies are supporting KNIME and writing their own extensions. Data analysts and data scientists are using the solution for ETI processes.

Overall, I rate KNIME an eight out of ten.